Abstract
With over 600 entries, this is by far the most comprehensive bibliography of the machine learning systems introduced by John Holland.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Emergent Computation. Proceedings of the Ninth Annual International Conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks. A special issue of Physica D. Stephanie Forrest (Ed.), 1990.
Collected Abstracts for the First International Workshop on Learning Classifier System (IWLCS92), 1992. October 6–8, NASA Johnson Space Center, Houston, Texas.
Proceedings of the 2000 Congress on Evolutionary Computation (CEC00). IEEE Press, 2000.
Proceedings of the International Workshop on Learning Classifier Systems (IWLCS-2000), in the Joint Workshops of SAB 2000 and PPSN 2000, 2000. Pier Luca Lanzi, Wolfgang Stolzmann and Stewart W. Wilson (workshop organisers).
Jose L. Aguilar and Mariela Cerrada. Reliability-Centered Maintenance Methodology-Based Fuzzy Classifier System Design for Fault Tolerance. In Koza et al. [345], page 621. One page paper.
Manu Ahluwalia and Larry Bull. A Genetic Programming-based Classifier System. In Banzhaf et al. [18], pages 11–18.
Rudolf F. Albrecht, Nigel C. Steele, and Colin R. Reeves, editors. Proceedings of the International Conference on Artificial Neural Nets and Genetic Algorithms. Spring-Verlag, 1993.
Peter J. Angeline, Zbyszek Michalewicz, Marc Schoenauer, Xin Yao, and Ali Zalzala, editors. Proceedings of the 1999 Congress on Evolutionary Computation CEC99, Washington (DC), 1999. IEEE Press.
W. Brian Arthur, John H. Holland, Blake LeBaron, Richard Palmer, and Paul Talyer. Asset Pricing Under Endogenous Expectations in an Artificial Stock Market. Technical report, Santa Fe Institute, 1996. This is the original version of LeBaron1999a.
Thomas Bäck, editor. Proceedings of the 7th International Conference on Genetic Algorithms (ICGA97). Morgan Kaufmann: San Francisco CA, 1997.
Thomas Bäck, David B. Fogel, and Zbigniew Michalewicz, editors. Handbook of Evolutionary Computation. Institute of Physics Publishing and Oxford University Press, 1997. http://www.iop.org/Books/Catalogue/.
Thomas Bäck, Ulrich Hammel, and Hans-Paul Schwefel. Evolutionary computation: Comments on the history and current state. IEEE Transactions on Evolutionary Computation, 1(1):3–17, 1997.
Jalal Baghdadchi. A Classifier Based Learning Model for Intelligent Agents. In Whitely et al. [586], page 870. One page poster paper.
Anthony J. Bagnall. A Multi-Adaptive Agent Model of Generator Bidding in the UK Market in Electricity. In Whitely et al. [586], pages 605–612.
Anthony J. Bagnall and G. D. Smith. An Adaptive Agent Model for Generator Company Bidding in the UK Power Pool. In Proceedings of Artificial Evolution, page ??, 1999.
Anthony J. Bagnall and G. D. Smith. Using an Adaptive Agent to Bid in a Simplified Model of the UK Market in Electricity. In Banzhaf et al. [18], page 774. One page poster paper.
N. R. Ball. Towards the Development of Cognitive Maps in Classifier Systems. In Albrecht et al. [7], pages 712–718.
Wolfgang Banzhaf, Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99). Morgan Kaufmann: San Francisco, CA, 1999.
Alwyn Barry. The Emergence of High Level Structure in Classifier Systems-A Proposal. Irish Journal of Psychology, 14(3):480–498, 1993.
Alwyn Barry. Hierarchy Formulation Within Classifiers System-A Review. In Goodman et al. [246], pages 195–211.
Alwyn Barry. Aliasing in XCS and the Consecutive State Problem: 1-Effects. In Banzhaf et al. [18], pages 19–26.
Alwyn Barry. Aliasing in XCS and the Consecutive State Problem: 2-Solutions. In Banzhaf et al. [18], pages 27–34.
Alwyn Barry. Specifying Action Persistence within XCS. In Whitely et al. [586], pages 50–57.
Alwyn Barry. XCS Performance and Population Structure within Multiple-Step Environments. PhD thesis, Queens University Belfast, 2000.
Richard J. Bauer. Genetic Algorithms and Investment Strategies. Wiley Finance Editions. John Wiley & Sons, 1994.
Eric Baum and Igor Durdanovic. An Evolutionary Post Production System. In Proceedings of the International Workshop on Learning Classifier Systems (IWLCS-2000), in the Joint Workshops of SAB 2000 and PPSN 2000 [4]. Extended abstract.
Richard K. Belew and Stephanie Forrest. Learning and Programming in Classifier Systems. Machine Learning, 3:193–223, 1988.
Richard K. Belew and Michael Gherrity. Back Propagation for the Classifier System. In Schaffer [463], pages 275–281.
Hugues Bersini and Francisco J. Varela. Hints for Adaptive Problem Solving Gleaned From Immune Networks. In Schwefel and Männer [470], pages 343–354.
Janine Beunings, Ludwig Bölkow, Bernd Heydemann, Biruta Kresling, Claus Peter Lieckfeld, Claus Mattheck, Werner Nachtigall, Josef Reichholf, Bertram J. Schmidt, Veronika Straaβ, and Reinhard Witt. Bionik: Natur als Vorbild. WWF Dokumentationen. PRO FUTURA Verlag, München, 1993.
J. Biondi. Robustness and evolution in an adaptive system application on classification task. In Albrecht et al. [7], pages 463–470.
Andrea Bonarini. ELF: Learning Incomplete Fuzzy Rule Sets for an Autonomous Robot. In Hans-Jürgen Zimmermann, editor, First European Congress on Fuzzy and Intelligent Technologies EUFIT’93, volume 1, pages 69–75, Aachen, D, September1993. Verlag der Augustinus Buchhandlung.
Andrea Bonarini. Evolutionary Learning of General Fuzzy Rules with Biased Evaluation Functions: Competition and Cooperation. Proc. 1st IEEE Conf. on Evolutionary Computation, pages 51–56, 1994.
Andrea Bonarini. Learning Behaviors Represented as Fuzzy Logic Controllers. In Hans-Jürgen Zimmermann, editor, Second European Congress on Intelligent Techniques and Soft Computing-EUFIT’94, volume 2, pages 710–715, Aachen, D, 1994. Verlag der Augustinus Buchhandlung.
Andrea Bonarini. Extending Q-learning to Fuzzy Classifier Systems. In Marco Gori and Giovanni Soda, editors, Proceedings of the Italian Association for Artificial Intelligence on Topics in Artificial Intelligence, volume 992 of LNAI, pages 25–36, Berlin, 1995. Springer.
Andrea Bonarini. Delayed Reinforcement, Fuzzy Q-Learning and Fuzzy Logic Controllers. In Herrera and Verdegay [274], pages 447–466.
Andrea Bonarini. Delayed Reinforcement, Fuzzy Q-Learning and Fuzzy Logic Controllers. In F. Herrera and J. L. Verdegay, editors, Genetic Algorithms and Soft Computing, (Studies in Fuzziness, 8), pages 447–466, Berlin, D, 1996. Physica-Verlag.
Andrea Bonarini. Evolutionary Learning of Fuzzy rules: competition and cooperation. In W. Pedrycz, editor, Fuzzy Modelling: Paradigms and Practice, pages 265–284. Norwell, MA: Kluwer Academic Press, 1996. ftp://ftp.elet.polimi.it/pub/Andrea.Bonarini/ELF/ELF-Pedrycz.ps.gz.
Andrea Bonarini. Anytime learning and adaptation of fuzzy logic behaviors. Adaptive Behavior, 5(3–4):281–315, 1997.
Andrea Bonarini. Reinforcement Distribution to Fuzzy Classifiers. In Proceedings of the IEEE World Congress on Computational Intelligence (WCCI)-Evolutionary Computation, pages 51–56. IEEE Computer Press, 1998.
Andrea Bonarini. Comparing reinforcement learning algorithms applied to crisp and fuzzy learning classifier systems. In Banzhaf et al. [18], pages 52–59.
Andrea Bonarini. An Introduction to Learning Fuzzy Classifier Systems. In Lanzi et al. [364], pages 83–104.
Andrea Bonarini and Filippo Basso. Learning to compose fuzzy behaviors for autonomous agents. Int. Journal of Approximate Reasoning, 17(4):409–432, 1997.
Andrea Bonarini, Claudio Bonacina, and Matteo Matteucci. Fuzzy and crisp representation of real-valued input for learning classifier systems. In Wu [623], pages 228–235.
Andrea Bonarini, Claudio Bonacina, and Matteo Matteucci. Fuzzy and Crisp Representations of Real-valued Input for Learning Classifier Systems. In Lanzi et al. [364], pages 107–124.
Andrea Bonarini, Marco Dorigo, V. Maniezzo, and D. Sorrenti. AutonoMouse: An Experiment in Grounded Behaviors. In Proceedings of GAA91-Second Italian Workshop on Machine Learning, Bari, Italy, 1991.
Pierre Bonelli and Alexandre Parodi. An Efficient Classifier System and its Experimental Comparison with two Representative learning methods on three medical domains. In Booker and Belew [59], pages 288–295.
Pierre Bonelli, Alexandre Parodi, Sandip Sen, and Stewart W. Wilson. NEW-BOOLE: A Fast GBML System. In International Conference on Machine Learning, pages 153–159, San Mateo, California, 1990. Morgan Kaufmann.
Lashon B. Booker. Intelligent Behavior as an Adaptation to the Task Environment. PhD thesis, The University of Michigan, 1982.
Lashon B. Booker. Improving the performance of genetic algorithms in classifier systems. In Grefenstette [250], pages 80–92.
Lashon B. Booker. Classifier Systems that Learn InternalWorld Models. Machine Learning, 3:161–192, 1988.
Lashon B. Booker. Triggered rule discovery in classifier systems. In Schaffer [463], pages 265–274.
Lashon B. Booker. Instinct as an Inductive Bias for Learning Behavioral Sequences. In Meyer and Wilson [385], pages 230–237.
Lashon B. Booker. Representing Attribute-Based Concepts in a Classifier System. In Rawlins [422], pages 115–127.
Lashon B. Booker. Viewing Classifier Systems as an Integrated Architecture. In Collected Abstracts for the First International Workshop on Learning Classifier System (IWLCS-92) [2]. October 6–8, NASA Johnson Space Center, Houston, Texas.
Lashon B. Booker. Do We Really Need to Estimate Rule Utilities in Classifier Systems? In Wu [623], pages 236–241.
Lashon B. Booker. Classifier systems, endogenous fitness, and delayed reward: A preliminary investigation. In Proceedings of the International Workshop on Learning Classifier Systems (IWLCS-2000), in the Joint Workshops of SAB 2000 and PPSN 2000 [4]. Extended abstract.
Lashon B. Booker. Do We Really Need to Estimate Rule Utilities in Classifier Systems? In Lanzi et al. [364], pages 125–142.
Lashon B. Booker and Richard K. Belew, editors. Proceedings of the 4th International Conference on Genetic Algorithms (ICGA91). Morgan Kaufmann: San Francisco CA, July 1991.
Lashon B. Booker, David E. Goldberg, and John H. Holland. Classifier Systems and Genetic Algorithms. Artificial Intelligence, 40:235–282, 1989.
Lashon B. Booker, Rick L. Riolo, and John H. Holland. Learning and Representation in Classifier Systems. In Vassant Honavar and Leonard Uhr, editors, Artificial Intelligence and Neural Networks, pages 581–613. Academic Press, 1994.
H. Brown Cribbs III and Robert E. Smith. Classifier System Renaissance: New Analogies, New Directions. In Koza et al. [347], pages 547–552.
Will Browne. The Development of an Industrial Learning Classifier System for Application to a Steel Hot Strip Mill. PhD thesis, University of Wales, Cardiff, 1999.
Will Browne, Karen Holford, and Carolynne Moore. An Industry Based Development of the Learning Classifier System Technique. Submitted to: 4th International Conference on Adaptive Computing in Design and Manufacturing (ACDM 2000).
Will Browne, Karen Holford, Carolynne Moore, and John Bullock. The implementation of a learning classifier system for parameter identification by signal processing of data from steel strip downcoilers. In A. T. Augousti, editor, Software in Measurement. IEE Computer and Control Division, 1996.
Will Browne, Karen Holford, Carolynne Moore, and John Bullock. A Practical Application of a Learning Classifier System for Downcoiler Decision Support in a Steel Hot Strip Mill. Ironmaking and Steelmaking, 25(1):33–41, 1997. Engineering Doctorate Seminar’ 97. Swansea, Wales, Sept. 2nd, 1997.
Will Browne, Karen Holford, Carolynne Moore, and John Bullock. A Practical Application of a Learning Classifier System in a Steel Hot Strip Mill. In Smith et al. [491], pages 611–614.
Will Browne, Karen Holford, Carolynne Moore, and John Bullock. An Industrial Learning Classifier System: The Importance of Pre-Processing Real Data and Choice of Alphabet. To appear in: Engineering Applications of Artificial Intelligence, 1999.
Larry Bull. Artificial Symbiology: evolution in cooperative multi-agent environments. PhD thesis, University of the West of England, 1995.
Larry Bull. On ZCS in Multi-agent Environments. Lecture Notes in Computer Science, 1498:471–480, 1998.
Larry Bull. On Evolving Social Systems. Computational and Mathematical Organization Theory, 5(3):281–298, 1999.
Larry Bull. On using ZCS in a Simulated Continuous Double-Auction Market. In Banzhaf et al. [18], pages 83–90.
Larry Bull. Simple markov models of the genetic algorithm in classifier systems: Accuracy-based fitness. In Proceedings of the International Workshop on Learning Classifier Systems (IWLCS-2000), in the Joint Workshops of SAB 2000 and PPSN 2000 [4]. Extended abstract.
Larry Bull. Simple markov models of the genetic algorithm in classifier systems: Multi-step tasks. In Proceedings of the International Workshop on Learning Classifier Systems (IWLCS-2000), in the Joint Workshops of SAB 2000 and PPSN 2000 [4]. Extended abstract.
Larry Bull and Terence C. Fogarty. Coevolving Communicating Classifier Systems for Tracking. In Albrecht et al. [7], pages 522–527.
Larry Bull and Terence C. Fogarty. Evolving Cooperative Communicating Classifier Systems. In A. V. Sebald and L. J. Fogel, editors, Proceedings of the Third Annual Conference on Evolutionary Programming, pages 308–315, 1994.
Larry Bull and Terence C. Fogarty. Parallel Evolution of Communicating Classifier Systems. In Proceedings of the 1994 IEEE Conference on Evolutionary Computing, pages 680–685. IEEE, 1994.
Larry Bull and Terence C. Fogarty. Evolutionary Computing in Cooperative Multi-Agent Systems. In Sandip Sen, editor, Proceedings of the 1996 AAAI Symposium on Adaptation, Coevolution and Learning in Multi-Agent Systems, pages 22–27. AAAI, 1996.
Larry Bull and Terence C. Fogarty. Evolutionary Computing in Multi-Agent Environments: Speciation and Symbiogenesis. In H-M. Voigt, W. Ebeling, I. Rechenberg, and H-P. Schwefel, editors, Parallel Problem Solving from Nature-PPSN IV, pages 12–21. Springer-Verlag, 1996.
Larry Bull, Terence C. Fogarty, S. Mikami, and J. G. Thomas. Adaptive Gait Acquisition using Multi-agent Learning for Wall Climbing Robots. In Automation and Robotics in Construction XII, pages 80–86, 1995.
Larry Bull, Terence C. Fogarty, and M. Snaith. Evolution in Multi-agent Systems: Evolving Communicating Classifier Systems for Gait in a Quadrupedal Robot. In Eshelman [186], pages 382–388.
Larry Bull and O. Holland. Internal and External Representations: A Comparison in Evolving the Ability to Count. In Proceedings of the First Annual Society for the Study of Artificial Intelligence and Simulated Behaviour Robotics Workshop, pages 11–14, 1994.
Larry Bull and Jacob Hurst. Self-Adaptive Mutation in ZCS Controllers. In Proceedings of the EvoNet Workshops-EvoRob 2000, pages 339–346. Springer, 2000.
Larry Bull, Jacob Hurst, and Andy Tomlinson. Mutation in Classifier System Controllers. In et al. [187], pages 460–467.
Martin Butz, David E. Goldberg, and Wolfgang Stolzmann. New challenges for an ACS: Hard problems and possible solutions. Technical Report 99019, University of Illinois at Urbana-Champaign, Urbana, IL, October 1999.
Martin Butz, David E. Goldberg, and Wolfgang Stolzmann. The anticipatory classifier system and genetic generalization. Technical Report 2000032, Illinois Genetic Algorithms Laboratory, 2000.
Martin Butz and Wolfgang Stolzmann. Action-Planning in Anticipatory Classifier Systems. In Wu [623], pages 242–249.
Martin V. Butz. An Implementation of the XCS classifier system in C. Technical Report 99021, The Illinois Genetic Algorithms Laboratory, 1999.
Martin V. Butz. XCSJava 1.0: An Implementation of the XCS classifier system in Java. Technical Report 2000027, Illinois Genetic Algorithms Laboratory, 2000.
Martin V. Butz, David E. Goldberg, and Wolfgang Stolzmann. Introducing a Genetic Generalization Pressure to the Anticipatory Classifier System-Part 1: Theoretical Approach. In Whitely et al. [586], pages 34–41. Also Technical Report 2000005 of the Illinois Genetic Algorithms Laboratory.
Martin V. Butz, David E. Goldberg, and Wolfgang Stolzmann. Introducing a Genetic Generalization Pressure to the Anticipatory Classifier System-Part 2: Performance Analysis. In Whitely et al. [586], pages 42–49. Also Technical Report2000006 of the Illinois Genetic Algorithms Laboratory.
Martin V. Butz, David E. Goldberg, and Wolfgang Stolzmann. Investigating Generalization in the Anticipatory Classifier System. In Proceedings of Parallel Problem Solving from Nature (PPSN VI), 2000. Also tech. report 2000014 of the Illinois Genetic Algorithms Laboratory.
Martin V. Butz, David E. Goldberg, and Wolfgang Stolzmann. Probabilityenhanced predictions in the anticipatory classifier system. In Proceedings of the International Workshop on Learning Classifier Systems (IWLCS-2000), in the Joint Workshops of SAB 2000 and PPSN 2000 [4]. Extended abstract.
Martin V. Butz and Stewart W. Wilson. An Algorithmic Description of XCS. Technical Report 2000017, Illinois Genetic Algorithms Laboratory, 2000.
Alessio Camilli. Classifier systems in massively parallel architectures. Master’s thesis, University of Pisa, 1990. (In Italian).
Alessio Camilli and Roberto Di Meglio. Sistemi a classificatori su architetture a parallelismo massiccio. Technical report, Univ. Delgi Studi di Pisa, 1989.
Alessio Camilli, Roberto Di Meglio, F. Baiardi, M. Vanneschi, D. Montanari, and R. Serra. Classifier System Parallelization on MIMD Architectures. Technical Report 3/17, CNR, 1990.
Y. J. Cao, N. Ireson, L. Bull, and R. Miles. Distributed Learning Control of Trafic Signals. In Proceedings of the EvoNet Workshops-EvoSCONDI 2000, pages 117–126. Springer, 2000.
Y. J. Cao, N. Ireson, Larry Bull, and R. Miles. Design of a Trafic Junction Controller using a Classifier System and Fuzzy Logic. In Proceedings of the Sixth International Conference on Computational Intelligence, Theory, and Applications. Springer-Verlag, 1999.
A. Carbonaro, G. Casadei, and A. Palareti. Genetic Algorithms and Classifier Systems in Simulating a Cooperative Behavior. In Albrecht et al. [7], pages 479–483.
Brian Carse. Learning Anticipatory Behaviour Using a Delayed Action Classifier System. In Fogarty [203], pages 210–223.
Brian Carse and Terence C. Fogarty. A delayed-action classifier system for learning in temporal environments. In Proceedings of the 1st IEEE Conference on Evolutionary Computation, volume 2, pages 670–673, 1994.
Brian Carse and Terence C. Fogarty. A Fuzzy Classifier System Using the Pittsburgh Approach. In Davidor and Schwefel [136], pages 260–269.
Brian Carse, Terence C. Fogarty, and A. Munro. Distributed Adaptive Routing Control in Communications Networks using a Temporal Fuzzy Classifier System. In Proceedings of the Fifth IEEE Conference on Fuzzy Systems, pages 2203–2207. IEEE, 1996.
Brian Carse, Terence C. Fogarty, and A. Munro. Evolutionary Learning of Controllers using Temporal Fuzzy Classifier Systems. In I. C. Parmee, editor, Proceedings of the Second Conference on Adaptive Computing in Engineering Design and Control, pages 174–180, 1996.
Brian Carse, Terence C. Fogarty, and A. Munro. Evolving fuzzy rule based controllers using genetic algorithms. International Journal for Fuzzy Sets and Systems, 80:273–293, 1996.
Brian Carse, Terence C. Fogarty, and A. Munro. The Temporal Fuzzy Classifier System and its Application to Distributed Control in a Homogeneous Multi-Agent ecology. In Goodman et al. [246], pages 76–86.
Brian Carse, Terence C. Fogarty, and Alistair Munro. Evolving Temporal Fuzzy Rule-Bases for Distributed Routing Control in Telecommunication Networks. In Herrera and Verdegay [274], pages 467–488.
Brian Carse, Terence C. Fogarty, and Alistair Munro. Artificial evolution of fuzzy rule bases which represent time: A temporal fuzzy classifier system. International Journal of Intelligent Systems, 13(issue 10–11):905–927, 1998.
G. Casadei, A. Palareti, and G. Proli. Classifier System in Trafic Management. In Albrecht et al. [7], pages 620–627.
Keith Chalk and George D. Smith. Multi-Agent Classifier Systems and the Iterated Prisoner’s Dilemma. In Smith et al. [491], pages 615–618.
Keith W. Chalk and George D. Smith. The Co-evolution of Classifier Systems in a Competitive Environment. Poster presented at AISB94. Authors were from the University of East Anglia, U.K.
Pawel Cichosz. Reinforcement learning algorithms based on the methods of temporal differences. Master’s thesis, Institute of Computer Science, Warsaw University of Technology, 1994.
Pawel Cichosz. Reinforcement Learning by Truncating Temporal Differences. PhD thesis, Department of Electronics and Information Technology,Warsaw University of Technology, 1997.
Pawel Cichosz and Jan J. Mulawka. GBQL: A novel genetics-based reinforcement learning architecture. In Proceedings of the Third European Congress on Intelligent Techniques and Soft Computing (EUFIT’95), 1995.
Pawel Cichosz and Jan J. Mulawka. Faster temporal credit assignment in learning classifier systems. In Proceedings of the First Polish Conference on Evolutionary Algorithms (KAE-96), 1996.
Dave Cliff and Seth G. Bullock. Adding `Foveal Vision’ to Wilson’s Animat. Adaptive Behavior, 2(1):47–70, 1993.
Dave Cliff, Philip Husbands, Jean-Arcady Meyer, and Stewart W. Wilson, editors. From Animals to Animats 3. Proceedings of the Third International Conference on Simulation of Adaptive Behavior (SAB94). A Bradford Book. MIT Press, 1994.
Dave Cliff and Susi Ross. Adding Temporary Memory to ZCS. Adaptive Behavior, 3(2):101–150, 1994. Also technical report: ftp://ftp.cogs.susx.ac.uk/pub/reports/csrp/csrp347.ps.Z.
Dave Cliff and Susi Ross. Adding Temporary Memory to ZCS. Technical Report CSRP347, School of Cognitive and Computing Sciences, University of Sussex, 1995. ftp://ftp.cogs.susx.ac.uk/pub/reports/csrp/csrp347.ps.Z.
H. G. Cobb and John J. Grefenstette. Learning the persistence of actions in reactive control rules. In Proceedings 8th International Machine Learning Workshop, pages 293–297. Morgan Kaufmann, 1991.
Philippe Collard and Cathy Escazut. Relational Schemata: A Way to Improve the Expressiveness of Classifiers. In Eshelman [186], pages 397–404.
Marco Colombetti and Marco Dorigo. Learning to Control an Autonomous Robot by Distributed Genetic Algorithms. In Roitblat and Wilson [447], pages 305–312.
Marco Colombetti and Marco Dorigo. Robot Shaping: Developing Situated Agents through Learning. Technical Report TR-92-040, International Computer Science Institute, Berkeley, CA, 1993.
Marco Colombetti and Marco Dorigo. Training Agents to Perform Sequential Behavior. Technical Report TR-93-023, International Computer Science Institute, Berkeley, CA, September 1993.
Marco Colombetti and Marco Dorigo. Training agents to perform sequential behavior. Adaptive Behavior, 2(3):247–275, 1994. ftp://iridia.ulb.ac.be/pub/dorigo/journals/IJ.06-ADAP94.ps.gz.
Marco Colombetti and Marco Dorigo. Verso un’ingegneria del comportamento. Rivista di Automatica, Elettronica e Informatica, 83(10), 1996. In Italian.
Marco Colombetti and Marco Dorigo. Evolutionary Computation in Behavior Engineering. In Evolutionary Computation: Theory and Applications, chapter 2, pages 37–80.World Scientific Publishing Co.: Singapore, 1999. Also Tech. Report. TR/IRIDIA/1996-1, IRIDIA, Université Libre de Bruxelles.
Marco Colombetti, Marco Dorigo, and G. Borghi. Behavior Analysis and Training: A Methodology for Behavior Engineering. IEEE Transactions on Systems, Man and Cybernetics, 26(6):365–380, 1996.
Marco Colombetti, Marco Dorigo, and G. Borghi. Robot shaping: The HAMSTER Experiment. In M. Jamshidi et al., editor, Proceedings of ISRAM’96, Sixth International Symposium on Robotics and Manufacturing, May 28–30, Montpellier, France, 1996.
M. Compiani, D. Montanari, R. Serra, and P. Simonini. Asymptotic dynamics of classifier systems. In Schaffer [463], pages 298–303.
M. Compiani, D. Montanari, R. Serra, and P. Simonini. Learning and Bucket Brigade Dynamics in Classifier Systems. In Special issue of Physica D (Vol. 42) [1], pages 202–212.
M. Compiani, D. Montanari, R. Serra, and G. Valastro. Classifier systems and neural networks. In Parallel Architectures and Neural Networks-First Italian Workshop, pages 105–118. World Scientific, Teaneck, NJ, 1989.
Clare Bates Congdon. Classification of epidemiological data: A comparison of genetic algorithm and decision tree approaches. In Proceedings of the 2000 Congress on Evolutionary Computation (CEC00) [3], pages 442–449.
O. Cordón, F. Herrera, E. Herrera-Viedma, and M. Lozano. Genetic Algorithms and Fuzzy Logic in Control Processes. Technical Report DECSAI-95109, University of Granada, Granada, Spain, 1995.
Y. Davidor and H.-P. Schwefel, editors. Parallel Problem Solving From Nature-PPSN III, volume 866 of Lecture Notes in Computer Science, Berlin, 1994. Springer Verlag.
Lawrence Davis. Mapping Classifier Systems into Neural Networks. In Proceedings of the Workshop on Neural Information Processing Systems 1, pages 49–56, 1988.
Lawrence Davis, editor. Genetic Algorithms and Simulated Annealing, Research Notes in Artificial Intelligence. Pitman Publishing: London, 1989.
Lawrence Davis. Mapping Neural Networks into Classifier Systems. In Schaffer [463], pages 375–378.
Lawrence Davis. Covering and Memory in Classifier Systems. In Collected Abstracts for the First International Workshop on Learning Classifier System (IWLCS-92) [2]. October 6–8, NASA Johnson Space Center, Houston, Texas.
Lawrence Davis and David Orvosh. The Mating Pool: A Testbed for Experiments in the Evolution of Symbol Systems. In Eshelman [186], pages 405–??
Lawrence Davis, Stewart W. Wilson, and David Orvosh. Temporary Memory for Examples can Speed Learning in a Simple Adaptive System. In Roitblat and Wilson [447], pages 313–320.
Lawrence Davis and D. K. Young. Classifier Systems with Hamming Weights. In Proceedings of the Fifth International Conference on Machine Learning, pages 162–173. Morgan Kaufmann, 1988.
Bart de Boer. Classifier Systems: a useful approach to machine learning? Master’s thesis, Leiden University, 1994. ftp://ftp.wi.leidenuniv.nl/pub/CS/MScTheses/deboer.94.ps.gz.
Kenneth A. De Jong. Learning with Genetic Algorithms: An Overview. Machine Learning, 3:121–138, 1988.
Michael de la Maza. A SEAGUL Visits the Race Track. In Schaffer [463], pages 208–212.
Daniel Derrig and James Johannes. Deleting End-of-Sequence Classifiers. In John R. Koza, editor, Late Breaking Papers at the Genetic Programming 1998 Conference, University of Wisconsin, Madison, Wisconsin, USA, July 1998. Stanford University Bookstore.
Daniel Derrig and James D. Johannes. Hierarchical Exemplar Based Credit Allocation for Genetic Classifier Systems. In Koza et al. [345], pages 622–628.
L. Desjarlais and Stephanie Forrest. Linked learning in classifier systems: A control architecture for mobile robots. In Collected Abstracts for the First International Workshop on Learning Classifier System (IWLCS-92) [2]. October 6–8, NASA Johnson Space Center, Houston, Texas.
P. Devine, R. Paton, and M. Amos. Adaptation of Evolutionary Agents in Computational Ecologies. In BCEC-97, Sweden, 1997.
Jean-Yves Donnart. Cognitive Architecture and Adaptive Properties of an Motivationally Autonomous Animat. PhD thesis, Université Pierre et Marie Curie. Paris, France., 1998. Thesis is in French.
Jean-Yves Donnart and Jean-Arcady Meyer. A hierarchical classifier system implementing a motivationally autonomous animat. In Cliff et al. [118], pages 144–153.
Jean-Yves Donnart and Jean-Arcady Meyer. Hierarchical-map Building and Selfpositioning with MonaLysa. Adaptive Behavior, 5(1):29–74, 1996.
Jean-Yves Donnart and Jean-Arcady Meyer. Learning Reactive and Planning Rules in a Motivationally Autonomous Animat. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics, 26(3):381–395, 1996.
Jean-Yves Donnart and Jean-Arcady Meyer. Spatial Exploration, Map Learning, and Self-Positioning with MonaLysa. In Maes et al. [377], pages 204–213.
Marco Dorigo. Message-Based Bucket Brigade: An Algorithm for the Apportionment of Credit Problem. In Y. Kodratoff, editor, Proceedings of European Working Session on Learning’ 91, Porto, Portugal, number 482 in Lecture notes in Artificial Intelligence, pages 235–244. Springer-Verlag, 1991.
Marco Dorigo. New perspectives about default hierarchies formation in learning classifier systems. In E. Ardizzone, E. Gaglio, and S. Sorbello, editors, Proceedings of the 2nd Congress of the Italian Association for Artificial Intelligence (AI*IA) on Trends in Artificial Intelligence, volume 549 of LNAI, pages 218–227, Palermo, Italy, October 1991. Springer Verlag.
Marco Dorigo. Using Transputers to Increase Speed and Flexibility of Genetic-based Machine Learning Systems. Microprocessing and Microprogramming, 34:147–152, 1991.
Marco Dorigo. Alecsys and the AutonoMouse: Learning to Control a Real Robot by Distributed Classifier System. Technical Report 92-011, Politecnico di Milano, 1992.
Marco Dorigo. Optimization, Learning and Natural Algorithms. PhD thesis, Politecnico di Milano, Italy, 1992. (In Italian).
Marco Dorigo. Genetic and Non-Genetic Operators in ALECSYS. Evolutionary Computation, 1(2):151–164, 1993. Also Tech. Report TR-92-075 International Computer Science Institute.
Marco Dorigo. Gli Algoritmi Genetici, i Sistemi a Classificatori e il Problema dell’Animat. Sistemi Intelligenti, 3(93):401–434, 1993. In Italian.
Marco Dorigo. Alecsys and the AutonoMouse: Learning to Control a Real Robot by Distributed Classifier Systems. Machine Learning, 19:209–240, 1995.
Marco Dorigo. The Robot Shaping Approach to Behavior Engineering. Thése d’Agrégation de l’Enseignement Supérieur, Faculté des Sciences Appliquées, Université Libre de Bruxelles, pp.176, 1995.
Marco Dorigo and Hugues Bersini. A Comparison of Q-Learning and Classifier Systems. In Cliff et al. [118], pages 248–255.
Marco Dorigo and Marco Colombetti. Robot shaping: Developing autonomous agents through learning. Artificial Intelligence, 2:321–370, 1994. ftp://iridia.ulb.ac.be/pub/dorigo/journals/IJ.05-AIJ94.ps.gz.
Marco Dorigo and Marco Colombetti. The Role of the Trainer in Reinforcement Learning. In S. Mahadevan et al., editor, Proceedings of MLC-COLT’ 94 Workshop on Robot Learning, July 10th, New Brunswick, NJ, pages 37–45, 1994.
Marco Dorigo and Marco Colombetti. Précis of Robot Shaping: An Experiment in Behavior Engineering. Special Issue on Complete Agent Learning in Complex Environments, Adaptive Behavior, 5(3–4):391–405, 1997.
Marco Dorigo and Marco Colombetti. Reply to Dario Floreano’s “Engineering Adaptive Behavior”. Special Issue on Complete Agent Learning in Complex Environments, Adaptive Behavior, 5(3–4):417–420, 1997.
Marco Dorigo and Marco Colombetti. Robot Shaping: An Experiment in Behavior Engineering. MIT Press/Bradford Books, 1998.
Marco Dorigo and V. Maniezzo. Parallel Genetic Algorithms: Introduction an Overview of Current Research. In J. Stenders, editor, Parallel Genetic Algorithms: Theory and Applications, Amsterdam, 1992. IOS Press.
Marco Dorigo, V. Maniezzo, and D. Montanari. Classifier-based robot control systems. In IFAC/IFIP/IMACS International Symposium on Artificial Intelligence in Real-Time Control, pages 591–598, Delft, Netherlands, 1992.
Marco Dorigo, Mukesh J. Patel, and Marco Colombetti. The effect of Sensory Information on Reinforcement Learning by a Robot Arm. In M. Jamshidi et al., editor, Proceedings of ISRAM’94, Fifth International Symposium on Robotics and Manufacturing, August 14–18, Maui, HI, pages 83–88. ASME Press, 1994.
Marco Dorigo and U. Schnepf. Organisation of Robot Behaviour Through Genetic Learning Processes. In Proceedings of ICAR’91-Fifth IEEE International Conference on Advanced Robotics, Pisa, Italy, pages 1456–1460. IEEE Press, 1991.
Marco Dorigo and U. Schnepf. Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis. IEEE Transactions on Systems, Man and Cybernetics, 23(1):141–154, 1993.
Marco Dorigo and E. Sirtori. A Parallel Environment for Learning Systems. In Proceedings of GAA91-Second Italian Workshop on Machine Learning, Bari, Italy, 1991.
Marco Dorigo and Enrico Sirtori. Alecsys: A Parallel Laboratory for Learning Classifier Systems. In Booker and Belew [59], pages 296–302.
Barry B. Druhan and Robert C. Mathews. THIYOS: A Classifier System Model of Implicit Knowledge in Artificial Grammars. In Proc. Ann. Cog. Sci. Soc., 1989.
John H. Holmes Dennis R. Durbin and Flaura K. Winston. A New Bootstrapping Method to Improve Classification Performance in Learning Classifier Systems. In Proceedings of Parallel Problem Solving from Nature (PPSN VI), 2000.
Daniel Eckert and Johann Mitlöhner. Modelling individual and endogenous learning in games: the relevance of classifier systems. In Complex Modelling for Socio-Economic Systems, SASA, Vienna, 1997.
Daniel Eckert, Johann Mitlöhner, and Makus Moschner. Evolutionary stability issues and adaptive learning in classifier systems. In OR’97 Conference on Operations Research, Vienna, 1997.
G. Enee and C. Escazut. Classifier systems evolving multi-agent system with distributed elitism. In Angeline et al. [8], pages 1740–1745.
Cathy Escazut and Philippe Collard. Learning Disjunctive Normal Forms in a Dual Classifier System. In Nada Lavrač and Stefan Wrobel, editors, Proceedings of the 8th European Conference on Machine Learning, volume 912 of LNAI, pages 271–274. Springer, 1995.
Cathy Escazut, Philippe Collard, and Jean-Louis Cavarero. Dynamic Management of the Specificity in Classifier Systems. In Albrecht et al. [7], pages 484–491.
Cathy Escazut and Terence C. Fogarty. Coevolving Classifier Systems to Control Trafic Signals. In John R. Koza, editor, Late Breaking Papers at the 1997 Genetic Programming Conference, Stanford University, CA, USA, July 1997. Stanford Bookstore.
Larry J. Eshelman, editor. Proceedings of the 6th International Conference on Genetic Algorithms (ICGA95). Morgan Kaufmann Publishers: San Francisco CA, 1995.
J. A. Meyer et al., editor. From Animals to Animats 6: Proceedings of the Sixth International Conference on Simulation of Adaptive Behavior, 2000.
Andrew Fairley and Derek F. Yates. Improving Simple Classifier Systems to alleviate the problems of Duplication, Subsumption and Equivalence of Rules. In Albrecht et al. [7], pages 408–416.
Andrew Fairley and Derek F. Yates. Inductive Operators and Rule Repair in a Hybrid Genetic Learning System: Some Initial Results. In Fogarty [203], pages 166–179.
I. De Falco, A. Iazzetta, E. Tarantino, and A. Della Cioppa. An evolutionary system for automatic explicit rule extraction. In Proceedings of the 2000 Congress on Evolutionary Computation (CEC00) [3], pages 450–457.
J. Doyne Farmer. A Rosetta Stone for Connectionism. In Special issue of Physica D (Vol. 42) [1], pages 153–187.
J. Doyne Farmer, N. H. Packard, and A. S. Perelson. The Immune System, Adaptation & Learning. Physica D, 22:187–204, 1986.
Francine Federman. NEXTNOTE: A Learning Classifier System. In Annie S. Wu, editor, Proceedings of the Genetic and Evolutionary Computation Conference Workshop Program, pages 136–138, 2000.
Francine Federman and Susan Fife Dorchak. Information Theory and NEXTPITCH: A Learning Classifier System. In Bäck [10], pages 442–449.
Francine Federman and Susan Fife Dorchak. Representation of Music in a Learning Classifier System. In Rad and Skowron, editors, Foundations of Intelligent Systems: Proceedings 10th International Symposium (ISMIS’97). Springer-Verlag: Heidelberg, 1997.
Francine Federman and Susan Fife Dorchak. A Study of Classifier Length and Population Size. In Koza et al. [345], pages 629–634.
Francine Federman, Gayle Sparkman, and Stephanie Watt. Representation of Music in a Learning Classifier System Utilizing Bach Chorales. In Banzhaf et al. [18], page 785. One page poster paper.
Rhonda Ficek. Genetic Algorithms. Technical Report NDSU-CS-TR-90-51, North Dakota State University. Computer Science and Operations Research, 1997.
M. V. Fidelis, H. S. Lopes, and A. A. Freitas. Discovering comprehensible classification rules with a genetic algorithm. In Proceedings of the 2000 Congress on Evolutionary Computation (CEC00) [3], pages 805–810.
Gary William Flake. The Computational Beauty of Nature. MIT Press, 1998. (Contains a chapter on ZCS).
Peter Fletcher. Simulating the use of ‘fiat money’ in a simple commodity economy. Master’s thesis, Schools of Psychology and Computer Science, University of Birmingham, 1996.
Terence C. Fogarty. Co-evolving Co-operative Populations of Rules in Learning Control Systems. In Evolutionary Computing, AISB Workshop Selected Papers [203], pages 195–209.
Terence C. Fogarty, editor. Evolutionary Computing, AISB Workshop Selected Papers, number 865 in Lecture Notes in Computer Science. Springer-Verlag, 1994.
Terence C. Fogarty. Learning new rules and adapting old ones with the genetic algorithm. In G. Rzevski, editor, Artificial Intelligence in Manufacturing, pages 275–290. Springer-Verlag, 1994.
Terence C. Fogarty. Optimising Individual Control Rules and Multiple Communicating Rule-based Control Systems with Parallel Distributed Genetic Algorithms. IEE Journal of Control Theory and Applications, 142(3):211–215, 1995.
Terence C. Fogarty, Larry Bull, and Brian Carse. Evolving Multi-Agent Systems. In J. Periaux and G. Winter, editors, Genetic Algorithms in Engineering and Computer Science, pages 3–22. John Wiley & Sons, 1995.
Terence C. Fogarty, Brian Carse, and Larry Bull. Classifier Systems-recent research. AISB Quarterly, 89:48–54, 1994.
Terence C. Fogarty, Brian Carse, and Larry Bull. Classifier Systems: selectionist reinforcement learning, fuzzy rules and communication. Presented at the First International Workshop on Biologically Inspired Evolutionary Systems, Tokyo, 1995.
Terence C. Fogarty, Brian Carse, and A. Munro. Artificial evolution of fuzzy rule bases which represent time: A temporal fuzzy classifier system. International Journal of Intelligent Systems, 13(10–11):906–927, 1998.
Terence C. Fogarty, N. S. Ireson, and Larry Bull. Genetic-based Machine Learning-Applications in Industry and Commerce. In Vic Rayward-Smith, editor, Applications of Modern Heuristic Methods, pages 91–110. Alfred Waller Ltd, 1995.
David B. Fogel. Evolutionary Computation. The Fossil Record. Selected Readings on the History of Evolutionary Computation, chapter 16: Classifier Systems. IEEE Press, 1998. This is a reprint of (Holland and Reitman, 1978), with an added introduction by Fogel.
Stephanie Forrest. A study of parallelism in the classifier system and its application to classification in KL-ONE semantic networks. PhD thesis, University of Michigan, Ann Arbor, MI, 1985.
Stephanie Forrest. Implementing semantic network structures using the classifier system. In Grefenstette [250], pages 24–44.
Stephanie Forrest. The Classifier System: A Computational Model that Supports Machine Intelligence. In International Conference on Parallel Processing, pages 711–716, Los Alamitos, Ca., USA, August 1986. IEEE Computer Society Press.
Stephanie Forrest. Parallelism and Programming in Classifier Systems. Pittman, London, 1991.
Stephanie Forrest, editor. Proceedings of the 5th International Conference on Genetic Algorithms (ICGA93). Morgan Kaufmann, 1993.
Stephanie Forrest and John H. Miller. Emergent behavior in classifier systems. In Special issue of Physica D (Vol. 42) [1], pages 213–217.
Stephanie Forrest, Robert E. Smith, and A. Perelson. Maintaining diversity with a genetic algorithm. In Collected Abstracts for the First International Workshop on Learning Classifier System (IWLCS-92) [2]. October 6–8, NASA Johnson Space Center, Houston, Texas.
Peter W. Frey and David J. Slate. Letter Recognition Using Holland-Style Adaptive Classifiers. Machine Learning, 6:161–182, 1991.
Leeann L. Fu. The XCS Classifier System and Q-learning. In John R. Koza, editor, Late Breaking Papers at the Genetic Programming 1998 Conference, University of Wisconsin, Madison, Wisconsin, USA, 1998. Stanford University Bookstore.
Leeann L. Fu. What I have come to understand about classifier systems, 1998. Unpublished document. Dept. of Electrical Engineering and Computer Science. University of Michigan.
Takeshi Furuhashi. A Proposal of Hierarchical Fuzzy Classifier Systems. In Forrest [216].
Takeshi Furuhashi, Ken Nakaoka, Koji Morikawa, and Yoshiki Uchikawa. Controlling Excessive Fuzziness in a Fuzzy Classifier System. In Forrest [216], pages 635–635.
Takeshi Furuhashi, Ken Nakaoka, and Yoshiki Uchikawa. A Study on Fuzzy Classifier System for Finding Control Knowledge of Multi-Input Systems. In Herrera and Verdegay [274], pages 489–502.
Santiago Garcia, Fermin Gonzalez, and Luciano Sanchez. Evolving Fuzzy Rule Based Classifiers with GAP: A Grammatical Approach. In Riccardo Poli, Peter Nordin, William B. Langdon, and Terence C. Fogarty, editors, Genetic Programming, Proceedings of EuroGP’99, volume 1598 of LNCS, pages 203–210, Goteborg, Sweden, May 1999. Springer-Verlag.
Chris Gathercole. A Classifier System Plays a Simple Board Game. Master’s thesis, Department of AI, University of Edinburgh, U.K., 1993.
Pierre Gerard and Olivier Sigaud. Combining Anticipation and Dynamic Programming in Classifier Systems. In Proceedings of the International Workshop on Learning Classifier Systems (IWLCS-2000), in the Joint Workshops of SAB 2000 and PPSN 2000 [4]. Extended abstract.
Andreas Geyer-Schulz. Fuzzy Classifier Systems. In Robert Lowen and Marc Roubens, editors, Fuzzy Logic: State of the Art, Series D: System Theory, Knowledge Engineering and Problem Solving, pages 345–354, Dordrecht, 1993. Kluwer Academic Publishers.
Andreas Geyer-Schulz. Fuzzy Rule-Based Expert Systems and Genetic Machine Learning. Physica Verlag, 1995. Book review at: http://www.apl.demon.co.uk/aplandj/fuzzy.htm
Andreas Geyer-Schulz. Holland Classifier Systems. In Proceedings of the International Conference on APL (APL’95), volume 25, pages 43–55, New York, NY, USA, June 1995. ACM Press.
Antonella Giani. A Study of Parallel Cooperative Classifier Systems. In John R. Koza, editor, Late Breaking Papers at the Genetic Programming 1998 Conference, University of Wisconsin, Madison, Wisconsin, USA, July 1998. Stanford University Bookstore.
Antonella Giani, Fabrizio Baiardi, and Antonina Starita. Q-Learning in Evolutionary Rule-Based Systems. In Davidor and Schwefel [136], pages 270–289.
Antonella Giani, A. Sticca, F. Baiardi, and A. Starita. Q-learning and Redundancy Reduction in Classifier Systems with Internal State. In Claire Nédellec and Céline Rouveirol editors, Proceedings of the 10th European Conference on Machine Learning (ECML-98), volume 1398 of LNAI, pages 364–369. Springer, 1998.
A. H. Gilbert, Frances Bell, and Christine L. Valenzuela. Adaptive Learning of Process Control and Profit Optimisation using a Classifier System. Evolutionary Computation, 3(2):177–198, 1995.
Attilio Giordana and Filippo Neri. Search-Intensive Concept Induction. Evolutionary Computation, 3:375–416, 1995.
Attilio Giordana and L. Saitta. REGAL: An integrated system for learning relations using genetic algorithms. In Proc. 2nd International Workshop on Multistrategy Learning, pages 234–249, 1993.
Attilio Giordana and L. Saitta. Learning disjunctive concepts by means of genetic algorithms. In Proc. Int. Conf. on Machine Learning, pages 96–104, 1994.
David E. Goldberg. Computer-Aided Gas Pipeline Operation using Genetic Algorithms and Rule Learning. PhD thesis, The University of Michigan, 1983.
David E. Goldberg. Dynamic System Control using Rule Learning and Genetic Algorithms. In Proceedings of the 9th International Joint Conference on Artificial Intelligence (IJCAI-85), pages 588–592. Morgan Kaufmann, 1985.
David E. Goldberg. Genetic algorithms and rules learning in dynamic system control. In Grefenstette [250], pages 8–15.
David E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, Mass., 1989.
David E. Goldberg. Probability Matching, the Magnitude of Reinforcement, and Classifier System Bidding. Machine Learning, 5:407–425, 1990. (Also TCGA tech report 88002, U. of Alabama).
David E. Goldberg. Some Reflections on Learning Classifier Systems. Technical Report 2000009, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, 2000. This appeared as part of Holland2000a.
David E. Goldberg, Jeffrey Horn, and Kalyanmoy Deb. What Makes a Problem Hard for a Classifier System? In Collected Abstracts for the First International Workshop on Learning Classifier System (IWLCS-92) [2]. (Also tech. report 92007 Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign). Available from ENCORE (ftp://ftp.krl.caltech.edu/pub/EC/Welcome.html) in the section on Classifier Systems.
S. Y. Goldsmith. Steady state analysis of a simple classifier system. PhD thesis, University of New Mexico, Albuquerque, USA, 1989.
E. G. Goodman, V. L. Uskov, and W. F. Punch, editors. Proceedings of the First International Conference on Evolutionary Algorithms and their Application EVCA’96, Moscow, 1996. The Presidium of the Russian Academy of Sciences.
David Perry Greene and Stephen F. Smith. Competition-based induction of decision models from examples. Machine Learning, 13:229–257, 1993.
David Perry Greene and Stephen F. Smith. Using Coverage as a Model Building Constraint in Learning Classifier Systems. Evolutionary Computation, 2(1):67–91, 1994.
A. Greenyer. The use of a learning classifier system JXCS. In P. van der Putten and M. van Someren, editors, CoIL Challenge 2000: The Insurance Company Case. June 2000. Technical report 2000-09, Leiden Institute of Advanced Computer Science.
John J. Grefenstette, editor. Proceedings of the 1st International Conference on Genetic Algorithms and their Applications (ICGA85). Lawrence Erlbaum Associates: Pittsburgh, PA, July 1985.
John J. Grefenstette. Multilevel Credit Assignment in a Genetic Learning System. In Proceedings of the 2nd International Conference on Genetic Algorithms (ICGA87) [252], pages 202–207.
John J. Grefenstette, editor. Proceedings of the 2nd International Conference on Genetic Algorithms (ICGA87), Cambridge, MA, July 1987. Lawrence Erlbaum Associates.
John J. Grefenstette. Credit Assignment in Rule Discovery Systems Based on Genetic Algorithms. Machine Learning, 3:225–245, 1988.
John J. Grefenstette. A System for Learning Control Strategies with Genetic Algorithms. In Schaffer [463], pages 183–190.
John J. Grefenstette. Lamarckian Learning in Multi-Agent Environments. In Booker and Belew [59], pages 303–310. http://www.ib3.gmu.edu/gref/publications.html.
John J. Grefenstette. Learning decision strategies with genetic algorithms. In Proc. Intl. Workshop on Analogical and Inductive Inference, volume 642 of Lecture Notes in Artificial Intelligence, pages 35–50. Springer-Verlag, 1992. http://www.ib3.gmu.edu/gref/.
John J. Grefenstette. The Evolution of Strategies for Multi-agent Environments. Adaptive Behavior, 1:65–89, 1992. http://www.ib3.gmu.edu/gref/.
John J. Grefenstette. Using a genetic algorithm to learn behaviors for autonomous vehicles. In Proceedings American Institute of Aeronautics and Astronautics Guidance, Navigation and Control Conference, pages 739–749. AIAA, 1992. http://www.ib3.gmu.edu/gref/.
John J. Grefenstette. Evolutionary Algorithms in Robotics. In M. Jamshediand C. Nguyen, editors, Robotics and Manufacturing: Recent Trends in Research, Education and Applications, v5. Proc. Fifth Intl. Symposium on Robotics and Manufacturing, ISRAM 94, pages 65–72. ASME Press: New York, 1994. http://www.ib3.gmu.edu/gref/.
John J. Grefenstette and H. G. Cobb. User’s guide for SAMUEL, Version 1.3. Technical Reportx NRL Memorandum Report 6820, Naval Research Laboratory, 1991.
John J. Grefenstette, C. L. Ramsey, and Alan C. Schultz. Learning Sequential Decision Rules using Simulation Models and Competition. Machine Learning, 5(4):355–381, 1990. http://www.ib3.gmu.edu/gref/publications.html.
John J. Grefenstette and Alan C. Schultz. An evolutionary approach to learning in robots. In Machine Learning Workshop on Robot Learning, New Brunswick, NJ, 1994. http://www.ib3.gmu.edu/gref/.
Hisashi Handa, Takashi Noda, Tadataka Konishi, Osamu Katai, and Mitsuru Baba. Coevolutionary fuzzy classifier system for autonomous mobile robots. In Takadama [531].
Adrian Hartley. Genetics Based Machine Learning as a Model of Perceptual Category Learning in Humans. Master’s thesis, University of Birmingham, 1998. ftp://ftp.cs.bham.ac.uk/pub/authors/T.Kovacs/index.html.
Adrian Hartley. Accuracy-based fitness allows similar performance to humans in static and dynamic classification environments. In Banzhaf et al. [18], pages 266–273.
U. Hartmann. Efficient Parallel Learning in Classifier Systems. In Albrecht et al. [7], pages 515–521.
U. Hartmann. On the Complexity of Learning in Classifier Systems. In Davidor and Schwefel [136], pages 280–289. Republished in: ECAI 94. 11th European Conference on Artificial Intelligence. A Cohn (Ed.), pp.438–442, 1994. John Wiley and Sons.
Marianne Haslev. A Classifier System for the Production by Computer of Past Tense Verb-Forms. Presented at a Genetic Algorithms Workshop at the Rowland Institute, Cambridge MA, Nov 1986, 1986.
Mozart Hasse and Aurora R. Pozo. Using Phenotypic Sharing in a Classifier Tool. In Whitely et al. [586], page 392. One page poster paper.
Akira Hayashi and Nobuo Suematsu. Viewing Classifier Systems as Model Free Learning in POMDPs. In Advances in Neural Information Processing Systems 11, pages 989–995, 1999.
Luis Miramontes Hercog. Hand-eye coordination: An evolutionary approach. Master’s thesis, Department of Artificial Intelligence. University of Edinburgh, 1998.
Luis Miramontes Hercog and Terence C. Fogarty. XCS-based inductive intelligent multi-agent system. In Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference (GECCO-2000), pages 125–132, 2000.
Luis Miramontes Hercog and Terence C. Fogarty. XCS-based Inductive Multi-Agent System. In Proceedings of the International Workshop on Learning Classifier Systems (IWLCS-2000), in the Joint Workshops of SAB 2000 and PPSN 2000 [4]. Extended abstract.
F. Herrera and J. L. Verdegay, editors. Genetic Algorithms and Soft Computing, (Studies in Fuzziness, 8). Physica-Verlag, Berlin, 1996.
E. Herrera-Viedma. Sistemas Clasificadores de Aprendizaje. Aproximaciones Difusas. Technical Report DECSAI-95132, Dept. of Computer Science and A.I., University of Granada, 1995.
M. R. Hilliard, G. E. Liepins, Mark Palmer, Michael Morrow, and Jon Richardson. A classifier based system for discovering scheduling heuristics. In Grefenstette [252], pages 231–235.
John H. Holland. Processing and processors for schemata. In E. L. Jacks, editor, Associative information processing, pages 127–146. New York: American Elsevier, 1971.
John H. Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, 1975. Republished by the MIT press, 1992.
John H. Holland. Adaptation. In R. Rosen and F. M. Snell, editors, Progress in theoretical biology. New York: Plenum, 1976.
John H. Holland. Adaptive algorithms for discovering and using general patterns in growing knowledge bases. International Journal of Policy Analysis and Information Systems, 4(3):245–268, 1980.
John H. Holland. Genetic Algorithms and Adaptation. Technical Report 34, University of Michigan. Department of Computer and Communication Sciences, Ann Arbor, 1981.
John H. Holland. Escaping brittleness. In Proceedings Second International Workshop on Machine Learning, pages 92–95, 1983.
John H. Holland. Properties of the bucket brigade. In Grefenstette [250], pages 1–7.
John H. Holland. A Mathematical Framework for Studying Learning in a Classifier System. In Doyne Farmer, Alan Lapedes, Norman Packard, and Burton Wendroff, editors, Evolution, Games and Learning: Models for Adaptation in Machines and Nature, pages 307–317, Amsterdam, 1986. North-Holland.
John H. Holland. A Mathematical Framework for Studying Learning in Classifier Systems. Physica D, 22:307–317, 1986.
John H. Holland. Escaping Brittleness: The possibilities of General-Purpose Learning Algorithms Applied to Parallel Rule-Based Systems. In Mitchell, Michalski, and Carbonell, editors, Machine learning, an artificial intelligence approach. Volume II, chapter 20, pages 593–623. Morgan Kaufmann, 1986.
John H. Holland. Genetic Algorithms and Classifier Systems: Foundations and Future Directions. In Grefenstette [252], pages 82–89.
John H. Holland. Concerning the Emergence of Tag-Mediated Lookahead in Classifier Systems. In Special issue of Physica D (Vol. 42) [1], pages 188–201.
John H. Holland, Lashon B. Booker, Marco Colombetti, Marco Dorigo, David E. Goldberg, Stephanie Forrest, Rick L. Riolo, Robert E. Smith, Pier Luca Lanzi, Wolfgang Stolzmann, and Stewart W. Wilson. What is a Learning Classifier System? In Lanzi et al. [364], pages 3–32.
John H. Holland and Arthur W. Burks. Adaptive Computing System Capable of Learning and Discovery. Patent 4697242 United States 29 Sept., 1987.
John H. Holland, Keith J. Holyoak, Richard E. Nisbett, and P. R. Thagard. Induction: Processes of Inference, Learning, and Discovery. MIT Press, Cambridge, 1986.
John H. Holland, Keith J. Holyoak, Richard E. Nisbett, and Paul R. Thagard. Classifier Systems, Q-Morphisms, and Induction. In Davis [138], pages 116–128.
John H. Holland and J. S. Reitman. Cognitive systems based on adaptive algorithms. In D. A. Waterman and F. Hayes-Roth, editors, Pattern-directed inference systems. New York: Academic Press, 1978. Reprinted in: Evolutionary Computation. The Fossil Record. David B. Fogel (Ed.) IEEE Press, 1998. ISBN: 0-7803-3481-7.
John H. Holmes. Evolution-Assisted Discovery of Sentinel Features in Epidemiologic Surveillance. PhD thesis, Drexel University, 1996. http://cceb.med.upenn.edu/holmes/disstxt.ps.gz.
John H. Holmes. A genetics-based machine learning approach to knowledge discovery in clinical data. Journal of the American Medical Informatics Association Supplement, 1996.
John H. Holmes. Discovering Risk of Disease with a Learning Classifier System. In Bäck [10]. http://cceb.med.upenn.edu/holmes/icga97.ps.gz.
John H. Holmes. Differential negative reinforcement improves classifier system learning rate in two-class problems with unequal base rates. In Koza et al. [345], pages 635–642. http://cceb.med.upenn.edu/holmes/gp98.ps.gz.
John H. Holmes. Evaluating Learning Classifier System Performance In Two-Choice Decision Tasks: An LCS Metric Toolkit. In Banzhaf et al. [18], page 789. One page poster paper.
John H. Holmes. Quantitative Methods for Evaluating Learning Classifier System Performance in Forced Two-Choice Decision Tasks. In Wu [623], pages 250–257.
John H. Holmes. Applying a Learning Classifier System to Mining Explanatory and Predictive Models from a Large Database. In Proceedings of the International Workshop on Learning Classifier Systems (IWLCS-2000), in the Joint Workshops of SAB 2000 and PPSN 2000 [4]. Extended abstract.
John H. Holmes. Learning Classifier Systems Applied to Knowledge Discovery in Clinical Research Databases. In Lanzi et al. [364], pages 243–261.
John H. Holmes, Dennis R. Durbin, and Flaura K. Winston. The learning classifier system: an evolutionary computation approach to knowledge discovery in epidemiologic surveillance. Artificial Intelligence In Medicine, 19(1):53–74, 2000.
Keith J. Holyoak, K. Koh, and Richard E. Nisbett. A Theory of Conditioning: Inductive Learning within Rule-Based Default Hierarchies. Psych. Review, 96:315–340, 1990.
Jeffrey Horn. The Nature of Niching: Genetic Algorithms and the Evolution of Optimal, Cooperative Populations. PhD thesis, University of Illinois at Urbana-Champaign (UMI Dissertation Service No. 9812622, 1997.
Jeffrey Horn and David E. Goldberg. Natural Niching for Cooperative Learning in Classifier Systems. In Koza et al. [347], pages 553–564.
Jeffrey Horn and David E. Goldberg. A Timing Analysis of Convergence to Fitness Sharing Equilibrium. In Parallel Problem Solving from Nature (PPSN), 1998.
Jeffrey Horn and David E. Goldberg. Towards a Control Map for Niching. In Foundations of Genetic Algorithms (FOGA), pages 287–310, 1998.
Jeffrey Horn, David E. Goldberg, and Kalyanmoy Deb. Implicit Niching in a Learning Classifier System: Nature’s Way. Evolutionary Computation, 2(1):37–66, 1994. Also IlliGAL Report No 94001, 1994.
Dijia Huang. A framework for the credit-apportionment process in rule-based systems. IEEE Transactions on Systems, Man and Cybernetics, 1989.
Dijia Huang. Credit Apportionment in Rule-Based Systems: Problem Analysis and Algorithm Synthesis. PhD thesis, University of Michigan, 1989.
Dijia Huang. The Context-Array Bucket-Brigade Algorithm: An Enhanced Approach to Credit-Apportionment in Classifier Systems. In Schaffer [463], pages 311–316.
Jacob Hurst and Larry Bull. A Self-Adaptive Classifier System. In Proceedings of the International Workshop on Learning Classifier Systems (IWLCS-2000), in the Joint Workshops of SAB 2000 and PPSN 2000 [4]. Extended abstract.
Francesc Xavier Llorà i Fàbrega. Automatic Classification using genetic algorithms under a Pittsburgh approach. Master’s thesis, Enginyeria La Salle-Ramon Llull University, 1998. http://www.salleurl.edu/~xevil/Work/index.html.
Francesc Xavier Llorà i Fàbrega and Josep Maria Garrell i Guiu. GENIFER: A Nearest Neighbour based Classifier System using GA. In Banzhaf et al. [18], page 797. One page poster paper appeared at GECCO. The full version is available at http://www.salleurl.edu/∼xevil/Work/index.html.
Francesc Xavier Llorà i Fàbrega, Josep Maria Garrell i Guiu, and Ester Bernadó i Mansilla. A Classifier System based on Genetic Algorithm under the Pittsburgh approach for problems with real valued attributes. In Viceng Torra, editor, Proceedings of Artificial Intelligence Catalan Workshop (CCIA98), volume 14–15, pages 85–93. ACIA Press, 1998. In Catalan http://www.salleurl.edu/~xevil/Work/index.html.
Josep Maria Garrell i Guiu, Elisabet Golobardes i Ribé, Ester Bernadó i Mansilla, and Francesc Xavier Llorà i Fàbrega. Automatic Classification of mammary biopsy images with machine learning techniques. In E. Alpaydin, editor, Proceedings of Engineering of Intelligent Systems (EIS’98), volume 3, pages 411–418. ICSC Academic Press, 1998. http://www.salleurl.edu/~xevil/Work/index.html.
Josep Maria Garrell i Guiu, Elisabet Golobardes i Ribé, Ester Bernadó i Mansilla, and Francesc Xavier Llorà i Fàbrega. Automatic Diagnosis with Genetic Algorithms and Case-Based Reasoning. To appear in AIENG Journal, 1999. (This is an expanded version of Guiu98a).
H. Iba, H. de Garis, and T. Higuchi. Evolutionary Learning of Predatory Behaviors Based on Structured Classifiers. In Roitblat and Wilson [447], pages 356–363.
H. Inoue, K. Takadama, M. Okada, K. Shimohara,, and O. Katai. Agent architecture based on self-reflection learning classifier system. In The 5th International Symposium on Artificial Life and Robotics (AROB’2000), pages 454–457, 2000.
H. Inoue, K. Takadama, and K. Shimohara. Inference of user’s internal states and its agent’s architecture. In The 20th System Engineering Meeting of SICE (The Society of Instrument and Control Engineers), pages 55–60, 2000.
N. Ireson, Y. J. Cao, L. Bull, and R. Miles. A Communication Architecture for Multi-Agent Learning Systems. In Proceedings of the EvoNet Workshops-EvoTel 2000, pages 255–266, 2000.
Hisao Ishibuchi and Tomoharu Nakashima. Linguistic Rule Extraction by Genetics-Based Machine Learning. In Whitely et al. [586], pages 195–202.
Yasushi Ishikawa and Takao Terano. Co-evolution of multiagents via organizational-learning classifier system and its application to marketing simulation. In Proc. 4th Pacific-Asia Conf. on Information Systems ( PACIS-2000), pages 1114–1127, 2000.
Kenneth A. De Jong and William M. Spears. Learning Concept Classification Rules using Genetic Algorithms. In Proceedings of the Twelfth International Conference on Artificial Intelligence IJCAI-91, volume 2, 1991.
K. Takadama, T. Terano, K. Shimohara, K. Hori and S. Nakasuka. Towards a multiagent design principle-analyzing an organizational-learning oriented classifier system. In V. Loia and S. Sessa, editors, Soft Computing Agents: New Trends for Designing Autonomous Systems, Series of Studies in Fuzziness and Soft Computing. Springer-Verlag, 2001.
Daisuke Katagami and Seiji Yamada. Real robot learning with human teaching. In Takadama [531].
Hiroharu Kawanaka, Tomohiro Yoshikawa, and Shinji Tsuruoka. A Study of Parallel GA Using DNA Coding Method for Acquisition of Fuzzy Control Rules. In Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference (GECCO-2000), pages 431–436, 2000.
Hiroaki Kitano, Stephen F. Smith, and Tetsuya Higuchi. GA-1: A Parallel Associative Memory Processor for Rule Learning with Genetic Algorithms. In Booker and Belew [59], pages 311–317.
Leslie Knight and Sandip Sen. PLEASE: A Prototype Learning System using Genetic Algorithms. In Eshelman [186], pages 429&#s2013;??
Kostyantyn Korovkin and Robert Richards. Visual Auction: A Classifier System Pedagogical and Researcher Tool. In Scott Brave and Annie S. Wu, editors, Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference (GECCO-99), pages 159–163, 1999.
Tim Kovacs. Evolving Optimal Populations with XCS Classifier Systems. Master’s thesis, School of Computer Science, University of Birmingham, Birmingham, U.K., 1996. Also tech. report CSR-96-17 and CSRP-96-17 ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1996/CSRP-96-17.ps.gz.
Tim Kovacs. Steady State Deletion Techniques in a Classifier System. Unpublished document-partially subsumed by Kovacs1999a ‘Deletion Schemes for Classifier Systems’, 1997.
Tim Kovacs. XCS Classifier System Reliably Evolves Accurate, Complete, and Minimal Representations for Boolean Functions. In Roy, Chawdhry, and Pant, editors, Soft Computing in Engineering Design and Manufacturing, pages 59–68. Springer-Verlag, London, 1997. ftp://ftp.cs.bham.ac.uk/pub/authors/T.Kovacs/index.html.
Tim Kovacs. XCS Classifier System Reliably Evolves Accurate, Complete, and Minimal Representations for Boolean Functions. Technical Report Version. Technical Report CSRP-97-19, School of Computer Science, University of Birming-ham, Birmingham, U.K., 1997. http://www.cs.bham.ac.uk/system/tech-reports/tr.html.
Tim Kovacs. Deletion schemes for classifier systems. In Banzhaf et al. [18], pages 329–336. Also technical report CSRP-99-08, School of Computer Science, University of Birmingham. http://www.cs.bham.ac.uk/∼tyk.
Tim Kovacs. Strength or accuracy? A comparison of two approaches to fitness calculation in learning classifier systems. In Wu [623], pages 258–265.
Tim Kovacs. Strength or Accuracy? Fitness calculation in learning classifier systems. In Lanzi et al. [364], pages 143–160.
Tim Kovacs. Towards a theory of strong overgeneral classifiers. In Terence C. Fogarty, Worthy Martin, and William M. Spears, editors, Proceedings of the Work-shop on Foundations of Genetic Algorithms (FOGA2000), 2000. Also tech. report CSRP-00-20, School of Computer Science, University of Birmingham.
Tim Kovacs and Manfred Kerber. Some dimensions of problem complexity for XCS. In Annie S. Wu, editor, Proceedings of the 2000 Genetic and Evolutionary Computation Conference Workshop Program, pages 289–292, 2000.
Tim Kovacs and Manfred Kerber. What makes a problem hard for XCS? In Proceedings of the International Workshop on Learning Classifier Systems (IWLCS-2000), in the Joint Workshops of SAB 2000 and PPSN 2000 [4]. Extended abstract.
Tim Kovacs and Manfred Kerber. What makes a problem hard for XCS? In Pier Luca Lanzi, Wolfgang Stolzmann, and Stewart W. Wilson, editors, Advances in Learning Classifier Systems, number 1996 in LNAI, page ?? Springer-Verlag, 2001.
Tim Kovacs and Pier Luca Lanzi. A Learning Classifier Systems Bibliography. Technical Report 99.52, Dipartimento di Elettronica e Informazione, Politecnico di Milano, 1999.
Tim Kovacs and Pier Luca Lanzi. A Learning Classifier Systems Bibliography. In Lanzi et al. [364], pages 321–347.
Yuhsuke Koyama. The emergence of the cooperative behaviors in a small group. In Takadama [531].
John R. Koza, Wolfgang Banzhaf, Kumar Chellapilla, Kalyanmoy Deb, Marco Dorigo, David B. Fogel, Max H. Garzon, David E. Goldberg, Hitoshi Iba, and Rick Riolo, editors. Genetic Programming 1998: Proceedings of the Third Annual Conference. Morgan Kaufmann: San Francisco, CA, 1998.
John R. Koza, Kalyanmoy Deb, Marco Dorigo, David B. Fogel, Max H. Garzon, Hitoshi Iba, and Rick Riolo, editors. Genetic Programming 1997: Proceedings of the Second Annual Conference. Morgan Kaufmann: San Francisco, CA, 1997.
John R. Koza, David E. Goldberg, David B. Fogel, and Rick L. Riolo, editors. Genetic Programming 1996: Proceedings of the First Annual Conference, Stanford University, CA, USA, 1996. MIT Press.
Pier Luca Lanzi. A Model of the Environment to Avoid Local Learning (An Analysis of the Generalization Mechanism of XCS). Technical Report 97.46, Politecnico di Milano. Department of Electronic Engineering and Information Sciences, 1997. http://ftp.elet.polimi.it/people/lanzi/report46.ps.gz.
Pier Luca Lanzi. A Study of the Generalization Capabilities of XCS. In Bäck [10], pages 418–425. http://ftp.elet.polimi.it/people/lanzi/icga97.ps.gz.
Pier Luca Lanzi. Solving Problems in Partially Observable Environments with Classifier Systems (Experiments on Adding Memory to XCS). Technical Report 97.45, Politecnico di Milano. Department of Electronic Engineering and Information Sciences, 1997. http://ftp.elet.polimi.it/people/lanzi/report45.ps.gz.
Pier Luca Lanzi. Adding Memory to XCS. In Proceedings of the IEEE Conference on Evolutionary Computation (ICEC98). IEEE Press, 1998. http://ftp.elet.polimi.it/people/lanzi/icec98.ps.gz.
Pier Luca Lanzi. An analysis of the memory mechanism of XCSM. In Koza et al. [345], pages 643–651. http://ftp.elet.polimi.it/people/lanzi/gp98.ps.gz.
Pier Luca Lanzi. Generalization in Wilson’s XCS. In A. E. Eiben, T. Bäck, M. Shoenauer, and H.-P Schwefel, editors, Proceedings of the Fifth International Conference on Parallel Problem Solving From Nature-PPSN V, number 1498 in LNCS. Springer Verlag, 1998.
Pier Luca Lanzi. Reinforcement Learning by Learning Classifier Systems. PhD thesis, Politecnico di Milano, 1998.
Pier Luca Lanzi. An Analysis of Generalization in the XCS Classifier System. Evolutionary Computation, 7(2):125–149, 1999.
Pier Luca Lanzi. Extending the Representation of Classifier Conditions Part I: From Binary to Messy Coding. In Banzhaf et al. [18], pages 337–344.
Pier LucaLanzi. Extending the Representation of Classifier Conditions Part II: From Messy Coding to S-Expressions. In Banzhaf et al. [18], pages 345–352.
Pier Luca Lanzi. Adaptive Agents with Reinforcement Learning and Internal Memory. In To appear in the Sixth International Conference on the Simulation of Adaptive Behavior (SAB2000), 2000.
Pier Luca Lanzi. Adaptive Agents with Reinforcement Learning and Internal Memory. In et al. [187], pages 333–342.
Pier Luca Lanzi. Learning Classifier Systems from a Reinforcement Learning Perspective. Technical Report 00-03, Dipartimento di Elettronica e Informazione, Politecnico di Milano, 2000.
Pier Luca Lanzi and Marco Colombetti. An Extension of XCS to Stochastic Environments. Technical Report 98.85, Dipartimento di Elettronica e Informazione-Politecnico di Milano, 1998.
Pier Luca Lanzi and Marco Colombetti. An Extension to the XCS Classifier System for Stochastic Environments. In Banzhaf et al. [18], pages 353–360.
Pier Luca Lanzi and Rick L. Riolo. A Roadmap to the Last Decade of Learning Classifier System Research (from 1989 to 1999). In Lanzi et al. [364], pages 33–62.
Pier Luca Lanzi, Wolfgang Stolzmann, and Stewart W. Wilson, editors. Learning Classifier Systems. From Foundations to Applications, volume 1813 of LNAI. Springer-Verlag, Berlin, 2000.
Pier Luca Lanzi and Stewart W. Wilson. Optimal classifier system performance in non-Markov environments. Technical Report 99.36, Dipartimento di Elettronica e Informazione-Politecnico di Milano, 1999. Also IlliGAL tech. report 99022, University of Illinois.
Pier Luca Lanzi and Stewart W. Wilson. Toward Optimal Performance in Classifier Systems. Evolutionary Computation, In press 2000.
Claude Lattaud. Non-Homogeneous Classifier Systems in a Macro-Evolution Process. In Wu [623], pages 266–271.
Claude Lattaud. Non-Homogeneous Classifier Systems in a Macro-Evolution Process. In Lanzi et al. [364], pages 161–174.
Blake Lebaron, W. Brian Arthur, and R. Palmer. The Time Series Properties of an Artificial Stock Market. Journal of Economic Dynamics and Control, 1999.
Martin Lettau and Harald Uhlig. Rules of Thumb and Dynamic Programming. Technical report, Department of Economics, Princeton University, 1994.
Martin Lettau and Harald Uhlig. Rules of thumb versus dynamic programming. American Economic Review, 89:148–174, 1999.
Gunar E. Liepins, M. R. Hillard, M. Palmer, and G. Rangarajan. Credit Assignment and Discovery in Classifier Systems. International Journal of Intelligent Systems, 6:55–69, 1991.
Gunar E. Liepins, Michael R. Hilliard, Mark Palmer, and Gita Rangarajan. Alternatives for Classifier System Credit Assignment. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89), pages 756–761, 1989.
Gunar E. Liepins and Lori A. Wang. Classifier System Learning of Boolean Concepts. In Booker and Belew [59], pages 318–323.
Derek A. Linkens and H. Okola Nyongesah. Genetic Algorithms for fuzzy control-Part II: Off-line system development and application. Technical Report CTA/94/2387/1st MS, Department of Automatic Control and System Engineering, University of Sheffield, U.K., 1994.
Juliet Juan Liu and James Tin-Yau Kwok. An extended genetic rule induction algorithm. In Proceedings of the 2000 Congress on Evolutionary Computation (CEC00) [3], pages 458–463.
Pattie Maes, Maja J. Mataric, Jean-Arcady Meyer, Jordan Pollack, and Stewart W. Wilson, editors. From Animals to Animats 4. Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior (SAB96). A Brad-ford Book. MIT Press, 1996.
Chikara Maezawa and Masayasu Atsumi. Collaborative Learning Agents with Structural Classifier Systems. In Banzhaf et al. [18], page 777. One page poster paper.
Bernard Manderick. Selectionist Categorization. In Schwefel and Männer [470], pages 326–330.
Ester Bernadó I Mansilla and Josep Maria Garrell i Guiu. MOLeCS: A Multi Objective Learning Classifier System. In Whitely et al. [586], page 390. One page poster paper.
Ramon Marimon, Ellen McGrattan, and Thomas J. Sargent. Money as a Medium of Exchange in an Economy with Arti_cially Intelligent Agents. Journal of Economic Dynamics and Control, 14:329–373, 1990. Also Tech. Report 89-004, Santa Fe Institute, 1989.
Maja J Mataric. A comparative analysis of reinforcement learning methods. A.I. Memo No. 1322, Massachusetts Institute of Technology, 1991.
Alaster D. McAulay and Jae Chan Oh. Image Learning Classifier System Using Genetic Algorithms. In Proceedings IEEE NAECON’ 89, 1989.
Chris Melhuish and Terence C. Fogarty. Applying A Restricted Mating Policy To Determine State Space Niches Using Immediate and Delayed Reinforcement. In Fogarty [203], pages 224–237.
J. A. Meyer and S. W. Wilson, editors. From Animals to Animats 1. Proceedings of the First International Conference on Simulation of Adaptive Behavior (SAB90). A Bradford Book. MIT Press, 1990.
Zbigniew Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, 1996. Contains introductory chapter on LCS.
John H. Miller and Stephanie Forrest. The dynamical behavior of classifier systems. In Schaffer [463], pages 304–310.
M. Mitchell and S. Forrest. Genetic Algorithms and Artificial Life. Technical Report 93-11-072, Santa Fe Institute, 1993. Contains a 2 page review of work on LCS.
Johann Mitlöhner. Classifier systems and economic modelling. In APL’ 96. Proceedings of the APL 96 conference on Designing the future, volume 26 (4), pages 77–86, 1996.
Chilukuri K. Mohan. Expert Systems: A Modern Overview. Kluwer, 2000. Contains an introductory survey chapter on LCS.
D. Montanari. Classifier systems with a constant-profile bucket brigade. In Collected Abstracts for the First International Workshop on Learning Classifier System (IWLCS-92) [2]. October 6–8, NASA Johnson Space Center, Houston, Texas.
David E. Moriarty, Alan C. Schultz, and John J. Grefenstette. Evolutionary Algorithms for Reinforcement Learning. Journal of Artificial Intelligence Research, 11:199–229, 1999. http://www.ib3.gmu.edu/gref/papers/moriarty-jair99.html.
Rémi Munos and Jocelyn Patinel. Reinforcement learning with dynamic covering of state-action space: Partitioning Q-learning. In Cliff et al. [118], pages 354–363.
Jorge Muruzábal. Fuzzy and Probabilistic Reasoning in Simple Learning Classifier Systems. In Proceedings of the 2nd IEEE International Conference on Evolutionary Computation, volume 1, pages 262–266. IEEE Press, 1995.
Jorge Muruzábal. Mining the space of generality with uncertainty-concerned cooperative classifiers. In Banzhaf et al. [18], pages 449–457.
Jorge Muruzábal and A. Muñoz. Diffuse pattern learning with Fuzzy ARTMAP and PASS. In Davidor and Schwefel [136], pages 376–385.
Ichiro Nagasaka and Toshiharu Taura. 3D Geometric Representation for Shape Generation using Classifier System. In Koza et al. [346], pages 515–520.
Filippo Neri. First Order Logic Concept Learning by means of a Distributed Genetic Algorithm. PhD thesis, University of Milano, Italy, 1997.
Filippo Neri. Comparing local search with respect to genetic evolution to detect intrusions in computer networks. In Proceedings of the 2000 Congress on Evolutionary Computation (CEC00) [3], pages 238–243.
Filippo Neri and Attilio Giordana. A distributed genetic algorithm for concept learning. In Eshelman [186], pages 436–443.
Filippo Neri and L. Saitta. Exploring the power of genetic search in learning symbolic classifiers. IEEE Trans. on Pattern Analysis and Machine Intelligence, PAMI-18:1135–1142, 1996.
Volker Nissen and Jörg Biethahn. Determining a Good Inventory Policy with a Genetic Algorithm. In Jörg Biethahn and Volker Nissen, editors, Evolutionary Algorithms in Management Applications, pages 240–249. Springer Verlag, 1995.
M. O. Odetayo and D. R. McGregor. Genetic algorithm for inducing control rules for a dynamic system. In Schaffer [463], pages 177–182. It could be argued this is a GA as opposed to a classifier system approach.
Jae Chan Oh. Improved Classifier System Using Genetic Algorithms. Master’s thesis, Wright State University, ??
Norihiko Ono and Adel T. Rahmani. Self-Organization of Communication in Distributed Learning Classifier Systems. In Albrecht et al. [7], pages 361–367.
G. Deon Oosthuizen. Machine Learning: A mathematical framework for neural network, symbolic and genetics-based learning. In Schaffer [463], pages 385–390.
F. Oppacher and D. Deugo. The Evolution of Hierarchical Representations. In Proceedings of the 3rd European Conference on Artificial Life. Springer-Verlag, 1995.
Alexandre Parodi and P. Bonelli. The Animat and the Physician. In Meyer and Wilson [385], pages 50–57.
Alexandre Parodi and Pierre Bonelli. A New Approach to Fuzzy Classifier Systems. In Forrest [216], pages 223–230.
Mukesh J. Patel, Marco Colombetti, and Marco Dorigo. Evolutionary Learning for Intelligent Automation: A Case Study. Intelligent Automation and Soft Computing, 1(1):29–42, 1995.
Mukesh J. Patel and Marco Dorigo. Adaptive Learning of a Robot Arm. In Fogarty [203], pages 180–194.
Mukesh J. Patel and U. Schnepf. Concept Formation as Emergent Phenomena. In Francisco J. Varela and P. Bourgine, editors, Proceedings First European Con ference on Artificial Life, pages 11–20. MIT Press, 1992.
Ray C. Paton. Designing Adaptable Systems through the Study and Application of Biological Sources. In Vic Rayward-Smith, editor, Applications of Modern Heuristic Methods, pages 39–54. Alfred Waller Ltd, 1995.
Rolf Pfeifer, Bruce Blumberg, Jean-Arcady Meyer, and Stewart W. Wilson, editors. From Animals to Animats 5. Proceedings of the Fifth International Conference on Simulation of Adaptive Behavior (SAB98). A Bradford Book. MIT Press, 1998.
Steven E. Phelan. Using Artificial Adaptive Agents to Explore Strategic Landscapes. PhD thesis, School of Business, Faculty of Law and Management, La Trobe University, Australia, 1997.
A. G. Pipe and Brian Carse. A Comparison between two Architectures for Searching and Learning in Maze Problems. In Fogarty [203], pages 238–249.
A. G. Pipe and Brian Carse. Autonomous Acquisition of Fuzzy Rules for Mobile Robot Control: First Results from two Evolutionary Computation Approaches. In Whitely et al. [586], pages 849–856.
R. Piroddi and R. Rusconi. A Parallel Classifier System to Solve Learning Problems. Master’s thesis, Dipartimento di Elettronica e Informazione, Politecnico di Milano, Milano, Italy., 1992.
Mitchell A. Potter, Kenneth A. De Jong, and John J. Grefenstette. A Coevolutionary Approach to Learning Sequential Decision Rules. In Eshelman [186], pages 366–372.
C. L. Ramsey and John J. Grefenstette. Case-based initialization of genetic algorithms. In Forrest [216], pages 84–91. http://www.ib3.gmu.edu/gref/.
C. L. Ramsey and John J. Grefenstette. Case-based anytime learning. In D. W. Aha, editor, Case-Based Reasoning: Papers from the 1994 Workshop. 1994. Also Tech. Report WS-94-07 http://www.ib3.gmu.edu/gref/.
Gregory J. E. Rawlins, editor. Proceedings of the First Workshop on Foundations of Genetic Algorithms (FOGA91). Morgan Kaufmann: San Mateo, 1991.
Robert A. Richards. Zeroth-Order Shape Optimization Utilizing a Learning Classifier System. PhD thesis, Stanford University, 1995. Online version available at: http://www-leland.stanford.edu/~buc/SPHINcsX/book.html.
Robert A. Richards. Classifier System Metrics: Graphical Depictions. In Koza et al. [345], pages 652–657.
Robert A. Richards and Sheri D. Sheppard. Classifier System Based Structural Component Shape Improvement Utilizing I-DEAS. In Iccon User’s Conference Proceeding. Iccon, 1992.
Robert A. Richards and Sheri D. Sheppard. Learning Classifier Systems in Design Optimization. In Design Theory and Methodology’ 92. The American Society of Mechanical Engineers, 1992.
Robert A. Richards and Sheri D. Sheppard. Two-dimensional Component Shape Improvement via Classifier System. In Artificial Intelligence in Design’ 92. Kluwer Academic Publishers, 1992.
Robert A. Richards and Sheri D. Sheppard. A Learning Classifier System for Three-dimensional Shape Optimization. In H. M. Voigt, W. Ebeling, I. Rechen-berg, and H. P. Schwefel, editors, Parallel Problem Solving from Nature-PPSN IV, volume 1141 of LNCS, pages 1032–1042. Springer-Verlag, 1996.
Robert A. Richards and Sheri D. Sheppard. Three-Dimensional Shape Optimization Utilizing a Learning Classifier System. In Koza et al. [347], pages 539–546.
Rick L. Riolo. Bucket Brigade Performance: I. Long Sequences of Classifiers. In Grefenstette [252], pages 184–195.
Rick L. Riolo. Bucket Brigade Performance: II. Default Hierarchies. In Grefenstette [252], pages 196–201.
Rick L. Riolo. CFS-C: A Package of Domain-Independent Subroutines for Implementing Classifier Systems in Arbitrary User-Defined Environments. Technical report, University of Michigan, 1988.
Rick L. Riolo. Empirical Studies of Default Hierarchies and Sequences of Rules in Learning Classifier Systems. PhD thesis, University of Michigan, 1988.
Rick L. Riolo. The Emergence of Coupled Sequences of Classifiers. In Schaffer [463], pages 256–264.
Rick L. Riolo. The Emergence of Default Hierarchies in Learning Classifier Systems. In Schaffer [463], pages 322–327.
Rick L. Riolo. Lookahead Planning and Latent Learning in a Classifier System. In Meyer and Wilson [385], pages 316–326.
Rick L. Riolo. Modelling Simple Human Category Learning with a Classifier System. In Booker and Belew [59], pages 324–333.
Rick L. Riolo. The discovery and use of forward models for adaptive classifier systems. In Collected Abstracts for the First International Workshop on Learning Classifier System (IWLCS-92) [2]. October 6–8, NASA Johnson Space Center, Houston, Texas.
Joaquin Rivera and Roberto Santana. Improving the Discovery Component of Classifier Systems by the Application of Estimation of Distribution Algorithms. In Proceedings of Student Sessions ACAI’99: Machine Learning and Applications, pages 43–44, Chania, Greece, July 1999.
A. Robert, F. Chantemargue, and M. Courant. Grounding Agents in EMud Artificial Worlds. In Proceedings of the First International Conference on Virtual Worlds, Paris (France), July 1–3, 1998.
Gary Roberts. A Rational Reconstruction of Wilson’s Animat and Holland’s CS-1. In Schaffer [463], pages 317–321.
Gary Roberts. Dynamic Planning for Classifier Systems. In Forrest [216], pages 231–237.
George G. Robertson. Parallel Implementation of Genetic Algorithms in a Classifier System. In Grefenstette [252], pages 140–147. Also Tech. Report TR-159 RL87-5 Thinking Machines Corporation.
George G. Robertson. Population Size in Classifier Systems. In Proceedings of the Fifth International Conference on Machine Learning, pages 142–152. Morgan Kaufmann, 1988.
George G. Robertson. Parallel Implementation of Genetic Algorithms in a Classifier System. In Davis [138], pages 129–140.
George G. Robertson and Rick L. Riolo. A Tale of Two Classifier Systems. Machine Learning, 3:139–159, 1988.
J. A. Meyer H. L. Roitblat and S. W. Wilson, editors. From Animals to Animats 2. Proceedings of the Second International Conference on Simulation of Adaptive Behavior (SAB92). A Bradford Book. MIT Press, 1992.
S. Ross. Accurate Reaction or Reflective Action? Master’s thesis, School of Cognitive and Computing Sciences, University of Sussex, 1994.
S. E. Rouwhorst and A. P. Engelbrecht. Searching the forest: Using decision trees as building blocks for evolutionary search in classification databases. In Proceedings of the 2000 Congress on Evolutionary Computation (CEC00)[3], pages 633–638.
A. Sanchis, J. M. Molina, P. Isasi, and J. Segovia. Knowledge acquisition including tags in a classifier system. In Angeline et al. [8], pages 137–144.
Adrian V. Sannier and Erik D. Goodman. Midgard: A Genetic Approach to Adaptive Load Balancing for Distributed Systems. In Proc. Fifth Intern. Conf. Machine Learning. Morgan Kaufmann, 1988.
Manuel Filipe Santos. Learning Classifiers in Distributed Environments. PhD thesis, Departamento de Sistemas de Informação, Universidade do Minho, Portugal, 2000.
Cédric Sanza, Christophe Destruel, and Yves Duthen. Agents autonomes pour l’interaction adaptative dans les mondes virtuels. In 5ème Journées de l’Association Francaise d’Informatique Graphique. Décembre 1997, Rennes, France, 1997. In French.
Cédric Sanza, Christophe Destruel, and Yves Duthen. A learning method for adaptation and evolution in virtual environments. In 3rd International Conference on Computer Graphics and Artificial Intelligence, April 1998, Limoges, France, 1998.
Cédric Sanza, Christophe Destruel, and Yves Duthen. Autonomous actors in an interactive real-time environment. In ICVC’99 International Conference on Visual Computing Feb. 1999, Goa, India, 1999.
Cédric Sanza, Christophe Destruel, and Yves Duthen. Learning in real-time environment based on classifiers system. In 7th International Conference in Central Europe on Computer Graphics, Visualization and Interactive Digital Media’99, Plzen, Czech Republic, 1999.
Cédric Sanza, Cyril Panatier, Hervé Luga, and Yves Duthen. Adaptive Behavior for Cooperation: a Virtual Reality Application. In 8th IEEE International Workshop on Robot and Human Interaction September 1999, Pisa, Italy, 1999.
Shaun Saxon and Alwyn Barry. XCS and the Monk’s problem. In Wu [623], pages 272–281.
Shaun Saxon and Alwyn Barry. XCS and the Monk’s Problems. In Lanzi et al. [364], pages 223–242.
Andreas Schachtner. A classifier system with integrated genetic operators. In H.-P. Schwefel and R. Männer, editors, Parallel Problem Solving from Nature, volume 496 of Lecture Notes in Computer Science, pages 331–337, Berlin, 1990. Springer.
J. David Schaffer. Some experiments in machine learning using vector evaluated genetic algorithms. PhD thesis, Vanderbilt University, Nashville, 1984.
J. David Schaffer. Learning Multiclass Pattern Discrimination. In Grefenstette [250], pages 74–79.
J. David Schaffer, editor. Proceedings of the 3rd International Conference on Genetic Algorithms (ICGA89), George Mason University, June 1989. Morgan Kaufmann.
Sonia Schulenburg and Peter Ross. An Adaptive Agent Based Economic Model. In Lanzi et al. [364], pages 263–282.
Sonia Schulenburg and Peter Ross. Strength and Money: An LCS Approach to Increasing Returns. In Proceedings of the International Workshop on Learning Classifier Systems (IWLCS-2000), in the Joint Workshops of SAB 2000 and PPSN 2000 [4]. Extended abstract.
Alan C. Schultz and John J. Grefenstette. Evolving Robot Behaviors. Poster at the Artificial Life Conference. http://www.ib3.gmu.edu/gref/.
Alan C. Schultz and John J. Grefenstette. Improving Tactical Plans with Genetic Algorithms. In Proceedings of the Second International Conference on Tools for Artificial Intelligence. IEEE, 1990.
Alan C. Schultz, Connie Logia Ramsey, and John J. Grefenstette. Simulation assisted learning by competition: Effects of noise differences between training model and target environment. In Proceedings of Seventh International Conference on Machine Learning (ICML), pages 211–215. Morgan Kaufmann, 1990.
Dale Schuurmans and Jonathan Schaeffer. Representational Dificulties with Classifier Systems. In Schaffer [463], pages 328–333. http://www.cs.ualberta.ca/~jonathan/Papers/Papers/classifier.ps.
Hans-Paul Schwefel and Reinhard Männer, editors. Parallel Problem Solving from Nature: Proceedings of the First International Workshop. Dortmund, FRG, 1–3 Oct 1990, number 496 in Lecture Notes in Computer Science, Heidelberg, 1990. Springer.
Tod A. Sedbrook, Haviland Wright, and Richard Wright. Application of a Genetic Classifier for Patient Triage. In Booker and Belew [59], pages 334–338.
Sandip Sen. Classifier system learning of multiplexer function. Dept. of ElectricalEngineering, University of Alabama, Tuscaloosa, Alabama. Class Project, 1988.
Sandip Sen. Sequential Boolean Function Learning by Classifier System. In Proc. of 1st International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, 1988.
Sandip Sen. Noise Sensitivity in a simple classifier system. In Proc. 5th Conf. on Neural Networks & Parallel Distributed Processing, 1992.
Sandip Sen. Improving classification accuracy through performance history. In Forrest [216], pages 652–652.
Sandip Sen. A Tale of two representations. In Proc. 7th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, pages 245–254, 1994.
Sandip Sen. Modelling human categorization by a simple classifier system. WSC1: 1st Online Workshop on Soft Computing. Aug 19–30, 1996. http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/papers/p020.html, 1996.
Sandip Sen and Mahendra Sekaran. Multiagent Coordination with Learning Classifier Systems. In Gerhard Weiβ and Sandip Sen, editors, Proceedings of the IJ-CAI Workshop on Adaption and Learning in Multi-Agent Systems, volume 1042 of LNAI, pages 218–233. Springer Verlag, 1996.
Tiago Sepulveda and Mario Rui Gomes. A Study on the Evolution of Learning Classifier Systems. In Proceedings of the International Workshop on Learning Classifier Systems (IWLCS-2000), in the Joint Workshops of SAB 2000 and PPSN 2000 [4]. Extended abstract.
F. Seredynski, Pawel Cichosz, and G. P. Klebus. Learning classifier systems in multi-agent environments. In Proceedings of the First IEE/IEEE International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications (GALESIA’95), 1995.
F. Seredynski and C. Z. Janikow. Learning nash equilibria by coevolving distributed classifier systems. In Angeline et al. [8], pages 1619–1626.
Sotaro Shimada and Yuichiro Anzai. Component-Based Adaptive Architecture with Classifier Systems. In Pfeifer et al. [414].
Sotaro Shimada and Yuichiro Anzai. Fast and Robust Convergence of Chained Classifiers by Generating Operons through Niche Formation. In Banzhaf et al. [18], page 810. One page poster paper.
Sotaro Shimada and Yuichiro Anzai. On Niche Formation and Corporation in Classifier System. In Takadama [531].
Takayuki Shiose and Tetsuo Sawaragi. Extended learning classifier systems by dual referencing mechanism. In Takadama [531].
Lingyan Shu and Jonathan Schaeffer. VCS: Variable Classifier System. In Schaffer [463], pages 334–339. http://www.cs.ualberta.ca/∼jonathan/Papers/Papers/vcs.ps.
Lingyan Shu and Jonathan Schaeffer. Improving the Performance of Genetic Algorithm Learning by Choosing a Good Initial Population. Technical Report TR-90-22, University of Alberta, CS DEPT, Edmonton, Alberta, Canada, 1990.
Lingyan Shu and Jonathan Schaeffer. HCS: Adding Hierarchies to Classifier Systems. In Booker and Belew [59], pages 339–345.
Olivier Sigaud. On the usefulness of a semi-automated Classifier System: the engineering perspective. In Proceedings of the International Workshop on Learning Classifier Systems (IWLCS-2000), in the Joint Workshops of SAB 2000 and PPSN 2000 [4]. Extended abstract.
George D. Smith. Economic Applications of Genetic Algorithms. In Vic Rayward-Smith, editor, Applications of Modern Heuristic Methods, pages 71–90. Alfred Waller Ltd, 1995. Contains 2 pages on LCS.
George D. Smith, Nigel C. Steele, and Rudolf F. Albrecht, editors. Artificial Neural Networks and Genetic Algorithms. Springer, 1997.
R. E. Smith, B. A. Dike, B. Ravichandran, A. El-Fallah, and R. K. Mehra. The Fighter Aircraft LCS: A Case of Different LCS Goals and Techniques. In Lanzi et al. [364], pages 283–300.
Robert E. Smith. Default Hierarchy Formation and Memory Exploitation in Learning Classifier Systems. PhD thesis, University of Alabama, 1991.
Robert E. Smith. A Report on The First International Workshop on Learning Classifier Systems (IWLCS-92). NASA Johnson Space Center, Houston, Texas, Oct. 6–9. ftp://lumpi.informatik.uni-dortmund.de/pub/LCS/papers/lcs92.ps.gz or from ENCORE, The Electronic Appendix to the Hitch-Hiker’s Guide to Evolutionary Computation (ftp://ftp.krl.caltech.edu/pub/EC/Welcome.html) in the section on Classifier Systems, 1992.
Robert E. Smith. Is a classifier system a type of neural network? In Collected Abstracts for the First International Workshop on Learning Classifier System (IWLCS-92) [2]. October 6–8, NASA Johnson Space Center, Houston, Texas.
Robert E. Smith. Memory exploitation in learning classifier systems. In Collected Abstracts for the First International Workshop on Learning Classifier System (IWLCS-92) [2]. October 6–8, NASA Johnson Space Center, Houston, Texas.
Robert E. Smith. Genetic Learning in Rule-Based and Neural Systems. In Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 1, page 183. NASA. Johnson Space Center, January 1993.
Robert E. Smith. Memory Exploitation in Learning Classifier Systems. Evolutionary Computation, 2(3):199–220, 1994.
Robert E. Smith. Derivative Methods: Learning Classifier Systems. In Bäck et al. [11], pages B1.2:6–B1.5:11. http://www.iop.org/Books/Catalogue/.
Robert E. Smith and H. Brown Cribbs. Is a Learning Classifier System a Type of Neural Network? Evolutionary Computation, 2(1):19–36, 1994.
Robert E. Smith and Henry Brown Cribbs. What Can I do with a Learning Classifier System?In C. Karr and L. M. Freeman, editors, Industrial Applications of Genetic Algorithms, pages 299–320. CRC Press, 1998.
Robert E. Smith, B. A. Dike, R. K. Mehra, B. Ravichandran, and A. El-Fallah. Classifier Systems in Combat: Two-sided Learning of Maneuvers for Advanced Fighter Aircraft. In Computer Methods in Applied Mechanics and Engineering. Elsevier, 1999.
Robert E. Smith, B. A. Dike, B. Ravichandran, A. El-Fallah, and R. K. Mehra. The Fighter Aircraft LCS: A Case of Different LCS Goals and Techniques. In Wu [623], pages 282–289.
Robert E. Smith, Stephanie Forrest, and A. S. Perelson. Searching for diverse, cooperative subpopulations with Genetic Algorithms. Evolutionary Computation, 1(2):127–149, 1993.
Robert E. Smith, Stephanie Forrest, and Alan S. Perelson. Population Diversity in an Immune System Model: Implications for Genetic Search. Technical report, Unknown institution, 1992.
Robert E. Smith and David E. Goldberg. Reinforcement Learning with Classifier Systems: Adaptive Default Hierarchy Formation. Technical Report 90002, TCGA, University of Alabama, 1990.
Robert E. Smith and David E. Goldberg. Variable Default Hierarchy Separation in a Classifier System. In Rawlins [422], pages 148–170.
Robert E. Smith and David E. Goldberg. Reinforcement learning with classifier systems: adaptative default hierarchy formation. Applied Artificial Intelligence, 6, 1992.
Robert E. Smith and H. B. Cribbs III. Cooperative Versus Competitive System Elements in Coevolutionary Systems. In Maes et al. [377], pages 497–505.
Robert E. Smith and H. B. Cribbs III. Combined biological paradigms. Robotics and Autonomous Systems, 22(1):65–74, 1997.
Robert E. Smith and Manuel Valenzuela-Rendón. A Study of Rule Set Development in a Learning Classifier System. In Schaffer [463], pages 340–346.
S. F. Smith.A Learning System Based on Genetic Adaptive Algorithms. PhD thesis, University of Pittsburgh, 1980.
S. F. Smith. Flexible Learning of Problem Solving Heuristics through Adaptive Search. In Proceedings Eight International Joint Conference on Artificial Intelli-gence, pages 422–425, 1983.
S. F. Smith and D. P. Greene. Cooperative Diversity using Coverage as a Constraint. In Collected Abstracts for the First International Workshop on Learning Classifier System (IWLCS-92) [2]. October 6–8, NASA Johnson Space Center, Houston, Texas.
Piet Spiessens. PCS: A Classifier System that Builds a Predictive Internal World Model. In PROC of the 9th European Conference on Artificial Intelligence, Stockholm, Sweden,Aug. 6–10, pages 622–627, 1990.
Bryan G. Spohn and Philip H. Crowley. Complexity of Strategies and the Evolution of Cooperation. In Koza et al. [346], pages 521–528.
Wolfgang Stolzmann. Learning Classifier Systems using the Cognitive Mechanism of Anticipatory Behavioral Control, detailed version. In Proceedings of the First European Workshop on Cognitive Modelling, pages 82–89. Berlin: TU, 1996. http://www.psychologie.uni-wuerzburg.de/stolzmann/.
Wolfgang Stolzmann. Antizipative Classifier Systeme. PhD thesis, Fachbereich Mathematik/Informatik, University of Osnabrueck, 1997.
Wolfgang Stolzmann. Two Applications of Anticipatory Classifier Systems (ACSs). In Proceedings of the 2nd European Conference on Cognitive Science, pages 68–73. Manchester, U.K., 1997. http://www.psychologie.uni-wuerzburg.de/stolzmann/.
Wolfgang Stolzmann. Anticipatory classifier systems. In Proceedings of the Third Annual Genetic Programming Conference, pages 658–664, San Francisco, CA, 1998. Morgan Kaufmann. http://www.psychologie.uni-wuerzburg.de/stolzmann/gp-98.ps.gz.
Wolfgang Stolzmann. Untersuchungen zur adäquatheit des postulats einer antizipativen verhaltenssteuerung zur erklärung von verhalten mit ACSs. In W. Krause and U. Kotkamp, editors,Intelligente Informationsverarbeitung, pages 130–138. Deutscher Universitäts Verlag, 1998.
Wolfgang Stolzmann. Latent Learning in Khepera Robots with Anticipatory Classifier Systems. In Wu [623], pages 290–297.
Wolfgang Stolzmann. An Introduction to Anticipatory Classifier Systems. In Lanzi et al. [364], pages 175–194.
Wolfgang Stolzmann and Martin Butz. Latent Learning and Action-Planning in Robots with Anticipatory Classifier Systems. In Lanzi et al. [364], pages 301–317.
Wolfgang Stolzmann, Martin Butz, J. Hoffmann, and D. E. Goldberg. First cognitive capabilities in the anticipatory classifier system. In et al. [187], pages 287–296. Also Technical Report 2000008 of the Illinois Genetic Algorithms Laboratory.
K. Takadama, H. Inoue, M. Okada, K. Shimohara,, and O. Katai. Agent architecture based on interactive self-reflection classifier system. International Journal of Artificial Life and Robotics (AROB), 2001.
K. Takadama, H. Inoue, and K. Shimohara. How to autonomously decide boundary between self and others? In The Third Asia-Pacific Conference on Simulated Evolution And Learning (SEAL’2000), 2000.
K. Takadama, S. Nakasuka, and T. Terano. Multiagent reinforcement learning with organizational-learning oriented classifier system. In The IEEE 1998 International Conference On Evolutionary Computation (ICEC’98), pages 63–68, 1998.
K. Takadama and T. Terano. Good solutions will emerge without a global objective function: Applying organizational-learning oriented classifier system to printed circuit board design. In The IEEE 1997 International Conference On Systems, Man and Cybernetics (SMC’97), pages 3355–3360, 1997.
K. Takadama, T. Terano, and K. Shimohara. Designing multiple agents using learning classifier systems. In The 4th Japan-Australia Joint Workshop on Intelligent and Evolutionary Systems (JA’2000), 2000.
Keiki Takadama, editor. Exploring New Potentials in Learning Classifier Systems. A Session of the 4th Japan-Australia Joint Workshop on Intelligent and Evolutionary Systems. Ashikaga Institute of Technology, 2000.
Keiki Takadama. Organizational-learning oriented classifier system. Technical Report TR-H-290, ATR, 2000. In Japanese.
Keiki Takadama, S. Nakasuka, and Takao Terano. On the credit assignment algorithm for organizational-learning oriented classifier system. In The 1997 System/information joint Symposium of SICE (The Society of Instrument and Control Engineers), pages 41–46, 1997. In Japanese.
Keiki Takadama, S. Nakasuka, and Takao Terano. Organizational-learning oriented classifier system. In The 11th Annual Conference of JSAI (Japanese Society for Artificial Intelligence), pages 201–204, 1997. In Japanese.
Keiki Takadama, S. Nakasuka, and Takao Terano. Organizational-learning ori-ented classifier system for intelligent multiagent systems. In The 6th Multi Agent and Cooperative Computation (MACC’ 97) of JSSST (Japan Society for Software Science and Technology), page ???, 1997. In Japanese.
Keiki Takadama, S. Nakasuka, and Takao Terano. Analyzing the roles of problem solving and learning in organizational-learning oriented classifier system. In H. Y. Lee and H. Motoda, editors, Lecture Notes in Artificial Intelligence, volume 1531, pages 71–82. Springer-Verlag, 1998.
Keiki Takadama, Shinichi Nakasuka, and Kasunori Shimohara. Designing multiple agents using learning classifier systems-suggestions from three levels analyses. In Takadama [531].
Keiki Takadama, Takao Terano, and Katsunori Shimohara. Agent-based model toward organizational computing: From organizational learning to genetics-based machine learning. In The IEEE 1999 International Conference On Systems, Man and Cybernetics (SMC’99), volume 2, pages 604–609, 1999.
Keiki Takadama, Takao Terano, and Katsunori Shimohara. Can multiagents learn in organization? analyzing organizational learning-oriented classifier system. In IJCAI’99 Workshop on Agents Learning about, from and other Agents, 1999.
Keiki Takadama, Takao Terano, and Katsunori Shimohara. Learning Classifier Systems meet Multiagent Environments. In Proceedings of the International Workshop on Learning Classifier Systems (IWLCS-2000), in the Joint Workshops of SAB 2000 and PPSN 2000 [4]. Extended abstract.
Keiki Takadama, Takao Terano, Katsunori Shimohara, H. Hori, and S. Nakasuka. Making Organizational Learning Operational: Implications from Learning Classifier System. Computational and Mathematical Organization Theory (CMOT), 5(3):229–252, 1999.
Keiki Takadama, Takao Terano, Katsunori Shimohara, H. Hori, and S. Nakasuka. Toward emergent problem solving by distributed classifier systems based on organizational learning. Transactions of SICE (the Society of Instrument and Control Engineers), 35(11):1486–1495, 1999. In Japanese.
Takao Terano and Z. Muro. On-the-fly knowledge base refinement by a classifier system. AI Communications, 4(2), 1994.
Takao Terano and Keiki Takadama. An organizational learning model of multia-gents with a learning classifier system. In The 1997 Fall Conference of JASMIN (Japan Society for Management Information), pages 128–131, 1997. In Japanese.
S. Tokinaga and A. B. Whinston. Applying Adaptive Credit Assignment Al-gorithm for the Learning Classifier System Based upon the Genetic Algorithm. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, VE75A(5):568–577, May 1992.
Andy Tomlinson. Corporate Classifier Systems. PhD thesis, University of the West of England, 1999.
Andy Tomlinson and Larry Bull. A Corporate Classifier System. In A. E. Eiben, T. Bäck, M. Shoenauer, and H.-P Schwefel, editors, Proceedings of the Fifth Inter-national Conference on Parallel Problem Solving From Nature-PPSN V, number 1498in LNCS, pages 550–559. Springer Verlag, 1998.
Andy Tomlinson and Larry Bull. A Corporate XCS. In Wu [623], pages 298–305.
Andy Tomlinson and Larry Bull. On Corporate Classifier Systems: Increasing the Benefits of Rule Linkage. In Banzhaf et al. [18], pages 649–656.
Andy Tomlinson and Larry Bull. A zeroth level corporate classifier system. In Wu [623], pages 306–313.
Andy Tomlinson and Larry Bull. A Corporate XCS. In Lanzi et al. [364], pages 194–208.
Kwok Ching Tsui and Mark Plumbley. A New Hillclimber for Classifier Systems. In GALESI97, 1997.
Patrick Tufts. Evolution of a Clustering Scheme for Classifier Systems: Beyond the Bucket Brigade. PhD Thesis proposal. http://www.cs.brandeis.edu/~zippy/papers.htm, 1994.
Patrick Tufts. Dynamic Classifiers: Genetic Programming and Classifier Systems. In E. V. Siegel and J. R. Koza, editors, Working Notes for the AAAI Symposium on Genetic Programming, pages 114–119, MIT, Cambridge, MA, USA, 1995. AAAI. Home page: http://www.cs.brandeis.edu/~zippy/papers.html.
Kirk Twardowski. Implementation of a Genetic Algorithm based Associative Classifier System (ACS). In Proceedings International Conference on Tools for Artificial Intelligence, 1990.
Kirk Twardowski. Credit Assignment for Pole Balancing with Learning Classifier Systems. In Forrest [216], pages 238–245.
Kirk Twardowski. An Associative Architecture for Genetic Algorithm-Based Machine Learning. Computer, 27(11):27–38, November 1994.
J. Urzelai, Dario Floreano, Marco Dorigo, and Marco Colombetti. Incremental Robot Shaping. Connection Science, 10(3–4):341–360, 1998.
J. Urzelai, Dario Floreano, Marco Dorigo, and Marco Colombetti. Incremental Robot Shaping. In Koza et al. [345], pages 832–840.
Manuel Valenzuela-Rendón. Boolean Analysis of Classifier Sets. In Schaffer [463], pages 351–358.
Manuel Valenzuela-Rendón. Two analysis tools to describe the operation of classifier systems. PhD thesis, University of Alabama, 1989. Also TCGA tech. report 89005.
Manuel Valenzuela-Rendón. The Fuzzy Classifier System: a Classifier System for Continuously Varying Variables. In Booker and Belew [59], pages 346–353.
Manuel Valenzuela-Rendón. The Fuzzy Classifier System: Motivations and First Results. Lecture Notes in Computer Science, 496:338–??, 1991.
Manuel Valenzuela-Rendón. Reinforcement learning in the fuzzy classifier system. In Collected Abstracts for the First International Workshop on Learning Classifier System (IWLCS-92) [2]. October 6–8, NASA Johnson Space Center, Houston, Texas.
Manuel Valenzuela-Rendón and Eduardo Uresti-Charre. A Non-Genetic Algorithm for Multiobjective Optimization. In Bäck [10], pages 658–665.
Terry van Belle. A New Approach to Genetic-Based Automatic Feature Discovery. Master’s thesis, University of Alberta, 1995. http://www.cs.ualberta.ca/~jonathan/.
Gilles Venturini. Apprentissage Adaptatif et Apprentissage Supervisé par Algorithme Génétique. PhD thesis, Université de Paris-Sud., 1994.
Nickolas Vriend. Self-Organization of Markets: An Example of a Computational Approach. Computational Economics, 8(3):205–231, 1995.
David Walter and Chilukuri K. Mohan. ClaDia: A Fuzzy Classifier System for Disease Diagnosis. In Proceedings of the 2000 Congress on Evolutionary Computation (CEC00) [3], pages 1429–1435.
L. A. Wang. Classifier System Learning of the Boolean Multiplexer Function. Master’s thesis, Computer Science Department, University of Tennessee, Knoxville, TN, 1990.
Gerhard Weiss. Action-oriented learning in classifier systems. Technical Report FKI-158-91, Technical Univ. München (TUM), 1991.
Gerhard Weiss. The Action-Oriented Bucket Brigade. Technical Report FKI-156-91, Technical Univ. München (TUM), 1991.
Gerhard Weiss. Hierarchical chunking in classifier systems. In Proceedings of the 12th National Conference on Artificial Intelligence, pages 1335–1340. AAAI Press/MIT Press, 1994.
Gerhard Weiss. Learning by chunking in reactive classifier systems. Technical report, Technical Univ. München (TUM), 1994.
Gerhard Weiss. The locality/globality dilemma in classifier systems and an approach to its solution. Technical Report FKI-187-94, Technical Univ. München (TUM), 1994.
Gerhard Weiss. The locality/globality dilemma in classi_er systems and an ap-proach to its solution. Technical Report FKI-187–94, Technical Univ. München (TUM), 1994.
Gerhard Weiss. An action-oriented perspective of learning in classifier systems. Journal of Experimental and Theoretical Artificial Intelligence, 8:43–62, 1996.
Thomas H. Westerdale. The bucket brigade is not genetic. In Grefenstette [250], pages 45–59.
Thomas H. Westerdale. A Reward Scheme for Production Systems with Over-lapping Conflict Sets. IEEE Transactions on Systems, Man and Cybernetics, SMC–16(3):369–383, 1986.
Thomas H. Westerdale. Altruism in the bucket brigade. In Grefenstette [252], pages 22–26.
Thomas H. Westerdale. A Defence of the Bucket Brigade. In Schaffer [463], pages 282–290.
Thomas H. Westerdale. Quasimorphisms or Queasymorphisms? Modelling Finite Automaton Environments. In Rawlins [422], pages 128–147.
Thomas H. Westerdale. Redundant Classifiers and Prokaryote Genomes. In Booker and Belew [59], pages 354–360.
Thomas H. Westerdale. Classifier Systems-No Wonder They Don’t Work. In Koza et al. [346], pages 529–537.
Thomas H. Westerdale. An Approach to Credit Assignment in Classifier Systems. Complexity, 4(2), 1999.
Thomas H. Westerdale. Wilson’s Error Measurement and the Markov Property — Identifying Detrimental Classifiers. In Wu [623], pages 314–321.
Darrell Whitely, David Goldberg, Erick Cantú-Paz, Lee Spector, Ian Parmee, and Hans-Georg Beyer, editors. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000). Morgan Kaufmann: San Francisco, CA, 2000.
Jason R. Wilcox. Organizational Learning within a Learning Classifier Systems. Master’s thesis, University of Illinois, 1995. Also Tech. Report No. 95003 IlliGAL.
Stewart W. Wilson. Aubert processing and intelligent vision. Technical report, Polaroid Corporation, 1981.
Stewart W. Wilson. On the retino-cortical mapping. International Journal of Man-Machine Studies, 18:361–389, 1983.
Stewart W. Wilson. Adaptive “cortical” pattern recognition. In Grefenstette [250], pages 188–196.
Stewart W. Wilson. Knowledge Growth in an Artificial Animal. In Grefenstette [250], pages 16–23. Also appeared in Proceedings of the 4th Yale.
Stewart W. Wilson. Knowledge Growth in an Artificial Animal. In Proceedings of the 4th Yale Workshop on Applications of Adaptive Systems Theory, pages 98–104, 1985.
Stewart W. Wilson. Classifier System Learning of a Boolean Function. Technical ReportRIS 27r, The Rowland Institute for Science, 1986.
Stewart W. Wilson. Knowledge Growth in an Artificial Animal. In K. S. Narenda, editor, Adaptive and learning systems: Theory and applications, pages 255–264. Plenum Press: New York, 1986.
Stewart W. Wilson. Classifier Systems and the Animat Problem. Machine Learn-ing, 2:199–228, 1987. Also Research Memo RIS-36r, the Rowland Institute for Science, Cambridge, MA, 1986.
Stewart W. Wilson. Hierarchical Credit Allocation in a Classifier System. In Proceedings Tenth International Joint Conference on AI (IJCAI-87), pages 217–220. Morgan Kaufmann Publishers, 1987. Also Research Memo RIS-37r, the Rowland Institute for Science, Cambridge, MA, 1986.
Stewart W. Wilson. Quasi-Darwinian Learning in a Classifier System. In Pro-ceedings of the Fourth International Workshop on Machine Learning, pages 59–65. Morgan Kaufmann, 1987.
Stewart W. Wilson. The genetic algorithm and biological development. In Grefen-stette[252], pages 247–251.
Stewart W. Wilson. Bid Competition and Specificity Reconsidered. Complex Systems, 2(6):705–723, 1988.
Stewart W. Wilson. Hierarchical Credit Assignment in a Classifier System. In M. Elzas, T. Oren, and B. P. Zeigler, editors, Modelling and Simulation Method-ology: Knowledge Systems Paradigms. North Holland, 1988.
Stewart W. Wilson. Hierarchical Credit Allocation in a Classifier System. In Davis [138], pages 104–115.
Stewart W. Wilson. Hierarchical credit allocation in a classifier system. In M. S. Elzas, T. I. Oren, and B. P. Zeigler, editors, Modelling and simulation methodol-ogy, pages 351–357. North-Holland: New York, 1989.
Stewart W. Wilson. The Genetic Algorithm and Simulated Evolution. In Chris Langton, editor, Artificial Life: Proceedings of an Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems, volume VIof Santa Fe Institute Studies in the Sciences of Complexity. Addison-Wesley: Reading, MA, 1989.
Stewart W. Wilson. Perceptron redux: Emergence of structure. In Special issue of Physica D (Vol. 42) [1], pages 249–256. Republished in Emergent Computation, S. Forrest (ed.), MIT Press/Bradford Books.
Stewart W. Wilson. The Animat Path to AI. In Meyer and Wilson [385], pages 15–21. http://prediction-dynamics.com/.
Stewart W. Wilson. Classifier System mapping of real vectors. In Collected Abstracts for the First International Workshop on Learning Classifier System (IWLCS-92) [2]. October 6–8, NASA Johnson Space Center, Houston, Texas.
Stewart W. Wilson. Toward a GA solution of the discovery problem. In Collected Abstracts for the First International Workshop on Learning Classifier System (IWLCS-92)[2]. October 6–8, NASA Johnson Space Center, Houston, Texas.
Stewart W. Wilson. ZCS: A zeroth level classifier system. Evolutionary Compu-tation, 2(1):1–18, 1994. http://prediction-dynamics.com/.
Stewart W. Wilson. Classifier Fitness Based on Accuracy. Evolutionary Compu-tation, 3(2):149–175, 1995. http://prediction-dynamics.com/.
Stewart W. Wilson. Explore/exploit strategies in autonomy. In Maes et al. [377], pages 325–332.
Stewart W. Wilson. Generalization in XCS. Unpublished contribution to the ICML’ 96 Workshop on Evolutionary Computing and Machine Learning. http://prediction-dynamics.com/, 1996.
Stewart W. Wilson. Generalization in evolutionary learning. Presented at the Fourth European Conference on Artificial Life (ECAL97), Brighton, UK, July 27–31. http://prediction-dynamics.com/, 1997.
Stewart W. Wilson. Generalization in the XCS classifier system. In Koza et al. [345], pages 665–674. http://prediction-dynamics.com/.
Stewart W. Wilson. Get real! XCS with continuous-valued inputs. In L. Booker, Stephanie Forrest, M. Mitchell, and Rick L. Riolo, editors, Festschrift in Honor of John H. Holland, pages 111–121. Center for the Study of Complex Systems, 1999. http://prediction-dynamics.com/.
Stewart W. Wilson. State of XCS classifier system research. In Wu [623], pages 322–334. Also Tech. Report 99.1.1, Prediction Dynamics, Concord MA. http://prediction-dynamics.com/.
Stewart W. Wilson. Get Real! XCS with Continuous-Valued Inputs. In Lanzi et al. [364], pages 209–219.
Stewart W. Wilson. Mining Oblique Data with XCS. In Proceedings of the International Workshop on Learning Classifier Systems (IWLCS-2000), in the Joint Workshops of SAB 2000 and PPSN 2000 [4]. Extended abstract.
Stewart W. Wilson. Mining Oblique Data with XCS. Technical Report 2000028, University of Illinois at Urbana-Champaign, 2000.
Stewart W. Wilson. State of XCS Classifier System Research. In Lanzi et al. [364], pages 63–82.
Stewart W. Wilson and David E. Goldberg. A Critical Review of Classifier Sys-tems. In Schaffer [463], pages 244–255. http://prediction-dynamics.com/.
Ian Wright. Reinforcement Learning and Animat Emotions. In Maes et al. [377], pages 272–281.
Ian Wright. Reinforcement learning and animat emotions. Technical Re-port CSRP-96–4, School of Computer Science. University of Birmingham, 1996. ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1996/CSRP-96-04.ps.gz.
Annie S. Wu, editor. Proceedings of the 1999 Genetic and Evolutionary Compu-tation Conference Workshop Program, 1999.
Derek F. Yates and Andrew Fairley. An Investigation into Possible Causes of, and Solutions to, Rule Strength Distortion Due to the Bucket Brigade Algorithm. In Forrest [216], pages 246–253.
Derek F. Yates and Andrew Fairley. Evolutionary Stability in Simple Classifier Systems. In Fogarty [203], pages 28–37.
Takahiro Yoshimi and Toshiharu Taura. Hierarchical Classifier System Based on the Concept of Viewpoint. In Koza et al. [345], pages 675–678.
Takahiro Yoshimi and Toshiharu Taura. A Computational Model of a Viewpoint-Forming Process in a Hierarchical Classifier System. In Banzhaf et al. [18], pages 758–766.
Zhaohua Zhang, Stan Franklin, and Dipankar Dasgupta. Metacognition in Soft-ware Agents Using Classifier Systems. In AAAI-98. Proceedings of the Fifteenth National Conference on Artificial Intelligence, pages 83–88, Madison (WI), 1998. AAAI-Press and MIT Press.
Hayong HarryZhou. Classifier systems with long term memory. In Grefenstette [250], pages 178–182.
Hayong Harry Zhou. CSM: A genetic classifier system with memory for learning by analogy. PhD thesis, Department of Computer Science, Vanderbilt University, Nashville, TN, 1987.
Hayong Harry Zhou. CSM: A Computational Model of Cumulative Learning. Machine Learning, 5(4):383–406, 1990.
Hayong Harry Zhou and John J. Grefenstette. Learning by Analogy in Genetic Classifier Systems. In Schaffer [463], pages 291–297.
Raed Abu Zitar and Mohammad H. Hassoun. Regulator Control via Genetic Search Assisted Reinforcement. In Forrest [216], pages 254–263.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kovacs, T., Luca Lanzi, P. (2001). A Bigger Learning Classifier Systems Bibliography. In: Luca Lanzi, P., Stolzmann, W., Wilson, S.W. (eds) Advances in Learning Classifier Systems. IWLCS 2000. Lecture Notes in Computer Science(), vol 1996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44640-0_14
Download citation
DOI: https://doi.org/10.1007/3-540-44640-0_14
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42437-6
Online ISBN: 978-3-540-44640-8
eBook Packages: Springer Book Archive